In your final repo, there should be an R markdown file that organizes all computational steps for evaluating your proposed Facial Expression Recognition framework.

This file is currently a template for running evaluation experiments. You should update it according to your codes but following precisely the same structure.

#Test Branch created
if(!require("EBImage")){
 install.packages("BiocManager")
 BiocManager::install("EBImage")
}
if(!require("R.matlab")){
  install.packages("R.matlab")
}
if(!require("readxl")){
  install.packages("readxl")
}

if(!require("dplyr")){
  install.packages("dplyr")
}
if(!require("readxl")){
  install.packages("readxl")
}

if(!require("ggplot2")){
  install.packages("ggplot2")
}

if(!require("caret")){
  install.packages("caret")
}

if(!require("glmnet")){
  install.packages("glmnet")
}

if(!require("WeightedROC")){
  install.packages("WeightedROC")
}

if(!require("gbm")){
  install.packages("gbm")
}

if(!require("DMwR")){
  install.packages("DMwR")
}

library(R.matlab)
library(readxl)
library(dplyr)
#library(EBImage)
library(ggplot2)
library(caret)
library(glmnet)
library(WeightedROC)
library(gbm)
library(DMwR)

New libraries

if(!require("randomForest")){
 install.packages("randomForest")
}
if(!require("xgboost")){
 install.packages("xgboost")
}
if(!require("tibble")){
 install.packages("tibble")
}
if(!require("ROSE")){
 install.packages("ROSE")
}
if(!require("ggplot2")){
 install.packages("ggplot2")
}
if(!require("tidyverse")){
 install.packages("tidyverse")
}

if(!require("AUC")){
 install.packages("AUC")
}
if(!require("e1071")){
 install.packages("e1071")
}
if(!require("OpenImageR")){
 install.packages("OpenImageR")
}
if(!require("caTools")){
  install.packages("caTools")
}
library(OpenImageR)
library(AUC)
library(e1071)
library(randomForest)
library(xgboost)
library(tibble)
library(ROSE)
library(ggplot2)
library(tidyverse)
library(AUC)
library(e1071)
library(caTools)
if(!require("prediction")){
  install.packages("prediction")
}
if(!require("pROC")){
  install.packages("pROC")
}
library(prediction)
library(pROC)

New libraries

<<<<<<< HEAD
```r
#train-test split
info <- read.csv(train_label_path)
n <- nrow(info)
n_train <- round(n*(4/5), 0)
train_idx <- sample(info$Index, n_train, replace = F)
test_idx <- setdiff(info$Index, train_idx)

<!-- rnb-source-end -->

<!-- rnb-chunk-end -->


<!-- rnb-text-begin -->



### Step 0 set work directories

<!-- rnb-text-end -->


<!-- rnb-chunk-begin -->


<!-- rnb-source-begin eyJkYXRhIjpbInNldC5zZWVkKDIwMjApIiwic2V0d2QoXCIuLi9kb2NcIikiLCIjIGhlcmUgcmVwbGFjZSBpdCB3aXRoIHlvdXIgb3duIHBhdGggb3IgbWFudWFsbHkgc2V0IGl0IGluIFJTdHVkaW8gdG8gd2hlcmUgdGhpcyBybWQgZmlsZSBpcyBsb2NhdGVkLiAiLCIjIHVzZSByZWxhdGl2ZSBwYXRoIGZvciByZXByb2R1Y2liaWxpdHkiXX0= -->

```r
set.seed(2020)
=======

set.seed(2020)
>>>>>>> 8034bda1773732b313f41b08d1963b46d0546d83
setwd("../doc")
# here replace it with your own path or manually set it in RStudio to where this rmd file is located. 
# use relative path for reproducibility

Provide directories for training images. Training images and Training fiducial points will be in different subfolders.

train_dir <- "../data/train_set/" # This will be modified for different data sets.
train_image_dir <- paste(train_dir, "images/", sep="")
train_pt_dir <- paste(train_dir,  "points/", sep="")
train_label_path <- paste(train_dir, "label.csv", sep="")

Step 1: set up controls for evaluation experiments.

In this chunk, we have a set of controls for the evaluation experiments.

  • (T/F) cross-validation on the training set
  • (T/F) reweighting the samples for training set
  • (number) K, the number of CV folds
  • (number) gbm.numtrees, the number of trees to use in GBM baseline
  • (T/F) process features for training set
  • (T/F) run evaluation on an independent test set
  • (T/F) process features for test set
  • (T/F) return polynomial features matrix only
  • (T/F) add polynomial features to starter code features matrix
  • (T/F) run gbm baseline model
  • <<<<<<< HEAD
run.cv <- TRUE # run cross-validation on the training set
=======
  • (0/1) alpha, alpha=0 for ridge regression, alpha=1 for lasso regression
  • (T/F) train ridge model
  • run.cv <- FALSE # run cross-validation on the training set
    >>>>>>> 8034bda1773732b313f41b08d1963b46d0546d83
    sample.reweight <- FALSE # run sample reweighting in model training
    K <- 5  # number of CV folds
    gbm.numtrees <- 1000 #number of trees to use in gbm
    run.feature.train <- TRUE # process features for training set
    run.test <- TRUE # run evaluation on an independent test set
    
    # gbm
    gbm.numtrees <- 1000 #number of trees to use in gbm
    run.feature.test <- TRUE # process features for test set
    run.poly.feature <- TRUE # process poly features
    run.add.poly.feature <- TRUE # and poly features to dist matrix
    run.gbm <- TRUE
    <<<<<<< HEAD
    
    # svm
    run.svm <- TRUE # svm is the chosen advanced model
    needs.balanced <- TRUE # balance data for model fitting
    model.selection <- TRUE # perform model selection on svm models
    
    # random forest
    run.balanced.data <- TRUE # Whether or not balance the data
    train.random.forest <- F # Train Random Forest Model
    tune.random.forest <- F # Tune Random Forest Model
    ======= needs.balanced <- TRUE # balance data for model fitting model.selection <- TRUE # perform model selection on svm models run.balanced.data <- TRUE # Whether or not balance the data train.random.forest <- F # Train Random Forest Model tune.random.forest <- F # Tune Random Forest Model alpha <- 0 # ridge regression train.ridge <- TRUE # train ridge model
    >>>>>>> 8034bda1773732b313f41b08d1963b46d0546d83

    Using cross-validation or independent test set evaluation, we compare the performance of models with different specifications. In this Starter Code, we tune parameter lambda (the amount of shrinkage) for logistic regression with LASSO penalty.

    lmbd = c(1e-3, 5e-3, 1e-2, 5e-2, 1e-1)
    model_labels = paste("LASSO Penalty with lambda =", lmbd)
    <<<<<<< HEAD =======

    The amount of the penalty for ridge regression can be fine-tuned using lambda.

    lambda = 10^seq(10, -2, length = 100)
    >>>>>>> 8034bda1773732b313f41b08d1963b46d0546d83

    Step 2: import data and train-test split

    #train-test split
    info <- read.csv(train_label_path)
    n <- nrow(info) #get number of rows from csv
    n_train <- round(n*(4/5), 0) #use 4/5 amount of data for training
    train_idx <- sample(info$Index, n_train, replace = F) #grab indexs used for training
    test_idx <- setdiff(info$Index, train_idx) # get indexs not used for training

    If you choose to extract features from images, such as using Gabor filter, R memory will exhaust all images are read together. The solution is to repeat reading a smaller batch(e.g 100) and process them.

    n_files <- length(list.files(train_image_dir,'*jpg'))
    
    # image_list <- list()
    # for(i in 1:100){
    #    image_list[[i]] <- readImage(paste0(train_image_dir, sprintf("%04d", i), ".jpg"))
    # }

    Fiducial points are stored in matlab format. In this step, we read them and store them in a list.

    #function to read fiducial points
    #input: index
    #output: matrix of fiducial points corresponding to the index
    readMat.matrix <- function(index){
         return(round(readMat(paste0(train_pt_dir, sprintf("%04d", index), ".mat"))[[1]],0))
    }
    
    #load fiducial points
    fiducial_pt_list <- lapply(1:n_files, readMat.matrix)
    save(fiducial_pt_list, file="../output/fiducial_pt_list.RData")

    Step 3: construct features and responses

    • The follow plots show how pairwise distance between fiducial points can work as feature for facial emotion recognition.

      • In the first column, 78 fiducials points of each emotion are marked in order.
      • In the second column distributions of vertical distance between right pupil(1) and right brow peak(21) are shown in histograms. For example, the distance of an angry face tends to be shorter than that of a surprised face.
      • The third column is the distributions of vertical distances between right mouth corner(50) and the midpoint of the upper lip(52). For example, the distance of an happy face tends to be shorter than that of a sad face.

    Figure1

    feature.R should be the wrapper for all your feature engineering functions and options. The function feature( ) should have options that correspond to different scenarios for your project and produces an R object that contains features and responses that are required by all the models you are going to evaluate later.

    • feature.R
    • Input: list of images or fiducial point
    • Output: an RData file that contains extracted features and corresponding responses
    <<<<<<< HEAD
    tm_feature_train <- NA
    =======
    
    
    source("../lib/feature.R")
    tm_feature_train <- NA
    >>>>>>> 8034bda1773732b313f41b08d1963b46d0546d83
    if(run.feature.train){
      tm_feature_train <- system.time(dat_train<-feature(fiducial_pt_list,train_idx, run.poly.feature, run.add.poly.feature))
      save(dat_train, file="../output/feature_train.RData")
    }else{
      load(file="../output/feature_train.RData")
    }
    <<<<<<< HEAD
    
    ======= tm_feature_test <- NA if(run.feature.test){ tm_feature_test <- system.time(dat_test <- feature(fiducial_pt_list, test_idx, run.poly.feature, run.add.poly.feature)) save(dat_test, file="../output/feature_test.RData") }else{ load(file="../output/feature_test.RData") } # transfer label column from factor to numeric dat_train$label <- as.numeric(dat_train$label)-1 dat_test$label <- as.numeric(dat_test$label)-1 #Rebalancing training data-Bootstrap Random Over-Sampling Examples Technique (ROSE) source if(run.balanced.data){ dat_train_balanced_rose<-ROSE(label~., dat_train,seed=2020)$data save(dat_train_balanced_rose, file="../output/balanced_data.RData") }else{ load(file="../output/balanced_data.RData") } table(dat_train_balanced_rose$label)
    >>>>>>> 8034bda1773732b313f41b08d1963b46d0546d83
    Error in feature(fiducial_pt_list, train_idx, run.poly.feature, run.add.poly.feature) : 
      unused arguments (run.poly.feature, run.add.poly.feature)
    Timing stopped at: 0.004 0.002 0.006

    Step 4: Train a classification model with training features and responses

    Call the train model and test model from library.

    train.R and test.R should be wrappers for all your model training steps and your classification/prediction steps.

    • train.R
      • Input: a data frame containing features and labels and a parameter list.
      • Output:a trained model
    • test.R
      • Input: the fitted classification model using training data and processed features from testing images
      • Input: an R object that contains a trained classifier.
      • Output: training model specification
    • In this Starter Code, we use logistic regression with LASSO penalty to do classification.
    source("../lib/train.R") 
    source("../lib/test.R")

    Model selection with cross-validation

    • Do model selection by choosing among different values of training model parameters.
    source("../lib/cross_validation.R")
    feature_train = as.matrix(dat_train[, 1:ncol(dat_train)-1])
    label_train = as.integer(dat_train$label)
    if(run.cv){
      res_cv <- matrix(0, nrow = length(lmbd), ncol = 4)
      for(i in 1:length(lmbd)){
        cat("lambda = ", lmbd[i], "\n")
        res_cv[i,] <- cv.function(features = feature_train, labels = label_train, K, 
                                  l = lmbd[i], reweight = sample.reweight)
      save(res_cv, file="../output/res_cv.RData")
      }
    }else{
      load("../output/res_cv.RData")
    }

    Visualize cross-validation results.

    # res_cv_rf  <- as.data.frame(res_cv_rf )
    # colnames(res_cv_rf ) <- c("mean_error", "sd_error", "mean_AUC", "sd_AUC")
    # res_cv_rf$k = as.factor(lmbd)
    # 
    # if(run.cv){
    #   p1 <- res_cv_rf  %>%
    #     ggplot(aes(x = as.factor(lmbd), y = mean_error,
    #                ymin = mean_error - sd_error, ymax = mean_error + sd_error)) +
    #     geom_crossbar() +
    #     theme(axis.text.x = element_text(angle = 90, hjust = 1))
    # 
    #   p2 <- res_cv_rf  %>%
    #     ggplot(aes(x = as.factor(lmbd), y = mean_AUC,
    #                ymin = mean_AUC - sd_AUC, ymax = mean_AUC + sd_AUC)) +
    #     geom_crossbar() +
    #     theme(axis.text.x = element_text(angle = 90, hjust = 1))
    # 
    #   print(p1)
    #   print(p2)
    # }
    # lambda=0.01 is the best
    • Choose the “best” parameter value
    #par_best <- lmbd[which.min(res_cv_rf$mean_error)] # lmbd[which.max(res_cv$mean_AUC)]

    Advanced Models:

    Create weight test

    label_test <- as.integer(dat_test$label)
    weight_test <- rep(NA, length(label_test))
    for (i in unique(label_test)){
      weight_test[label_test == i] = 0.5 * length(label_test) / length(label_test[label_test == i])
    }
    # training weights
    weight_train <- rep(NA, length(label_train))
    for (v in unique(label_train)){
      weight_train[label_train == v] = 0.5 * length(label_train) / length(label_train[label_train == v])
    }
    
    if (run.gbm){
      if (sample.reweight){
        tm_train <- system.time(fit_train <- train_gbm(dat_train, s=0.1, K=K, n=gbm.numtrees,w = weight_train))
      } else {
        tm_train <- system.time(fit_train <- train_gbm(dat_train, s=0.1, K=K, n=gbm.numtrees,w = NULL))
      }
      
      # plot the performance
      best.iter.oob <- gbm.perf(fit_train,method="OOB")  # returns out-of-bag estimated best number of trees
      print(best.iter.oob)
      best.iter.cv <- gbm.perf(fit_train,method="cv")   # returns K-fold cv estimate of best number of trees
      print(best.iter.cv)
    
    } else {
      if (sample.reweight){
        tm_train <- system.time(fit_train <- train(feature_train, label_train, w = weight_train, par_best))
      } else {
        tm_train <- system.time(fit_train <- train(feature_train, label_train, w = NULL, par_best))
      }
    }
    save(fit_train, file="../output/fit_train.RData")

    Step 5: Run test on test images

    tm_test = NA
    feature_test <- as.matrix(dat_test[, 1:ncol(dat_test)-1])
    if(run.test){
      load(file="../output/fit_train.RData")
      if (run.gbm){
        tm_test <- system.time(prob_pred<-test_gbm(fit_train,as.data.frame(feature_test),n=best.iter.cv,pred.type = 'response'))
        
        label_pred <- colnames(prob_pred)[apply(prob_pred, 1, which.max)]
        
      } else {
        tm_test <- system.time({label_pred <- as.integer(test(fit_train, feature_test, pred.type = 'class')); 
                                prob_pred <- test(fit_train, feature_test, pred.type = 'response')})  
      }
      
    }

    Random Forest:

    Tune RF

    <<<<<<< HEAD
    source("../lib/random_forest.R")
    if(tune.random.forest){
    time.rf.tune <- system.time(rf.tune <- random_forest_tune(dat_train_balanced_rose))
    save(rf.tune, file="../output/rf_tune.RData")
    }else(
      load("../output/rf_tune.RData")
    )
    rf.tune

    mtry = 154 is the best.

    Find the best ntrees

    =======
    source("../lib/random_forest.R")
    if(tune.random.forest){
    time.rf.tune <- system.time(rf.tune <- random_forest_tune(dat_train_balanced_rose))
    save(rf.tune, file="../output/rf_tune.RData")
    }else(
      load("../output/rf_tune.RData")
    )
    rf.tune

    mtry = 154 is the best.

    Find the best ntrees

    >>>>>>> 8034bda1773732b313f41b08d1963b46d0546d83
    source("../lib/random_forest.R")
    
    #Train 500
    if(tune.random.forest){
    time.rf.train <- system.time(random_forest_fit_500 <- random_forest_train_500(dat_train_balanced_rose,mtry = 154))
    save(random_forest_fit_500, file = "../output/rf_train_500_trees.RData")
    }
    #Test 500
    random_forest_test_prep=NA
    if(tune.random.forest){
     load(file="../output/rf_train_500_trees.RData")
     time.rf.test <- system.time(
       random_forest_test_prep <- random_forest_test(
         model = random_forest_fit_500,testset = dat_test)
                   )
    
    random_forest_test_prep <- as.numeric(as.character(random_forest_test_prep))
    accu_rf_test <- mean(random_forest_test_prep == dat_test$label)
    random_forest_label<-round(random_forest_test_prep)
    accu_rf <- sum(weight_test * (random_forest_label == label_test)) / sum(weight_test)
    #prob_pred <- lable_pred
    tpr.fpr <- WeightedROC(random_forest_test_prep, label_test, weight_test)
    auc_rf <- WeightedAUC(tpr.fpr)
    cat("The AUC of model after reweighting: RF", "is", auc_rf, ".\n")
    cat("The accuracy of model: Random Forest on imbalanced testing data", "is", accu_rf_test*100, "%.\n")
    cat("The accuracy of model: Random Forest on balanced testing data", "is", accu_rf*100, "%.\n")
    cat("Time for training model Random Forest = ", time.rf.train[1], "s \n")
    cat("Time for testing model Random Forest = ",time.rf.test[1], "s \n")
    }
    # The AUC of model after reweighting: RF is 0.5031999 .
    # The accuracy of model: Random Forest on imbalanced testing data is 80.33333 %.
    # The accuracy of model: Random Forest on balanced testing data is 50.31999 %.
    # Time for training model Random Forest =  20.95 s 
    # Time for testing model Random Forest =  0.09 s 
    
    #Train 1000
    if(tune.random.forest){
    time.rf.train <- system.time(random_forest_fit_1000 <- random_forest_train_1000(dat_train_balanced_rose,mtry = 154))
    save(random_forest_fit_1000, file = "../output/rf_train_1000_trees.RData")
    }
    #Test 1000
    random_forest_test_prep=NA
    if(tune.random.forest){
     load(file="../output/rf_train_1000_trees.RData")
     time.rf.test <- system.time(
       random_forest_test_prep <- random_forest_test(
         model = random_forest_fit_1000,testset = dat_test)
                   )
    
    random_forest_test_prep <- as.numeric(as.character(random_forest_test_prep))
    accu_rf_test <- mean(random_forest_test_prep == dat_test$label)
    random_forest_label<-round(random_forest_test_prep)
    accu_rf <- sum(weight_test * (random_forest_label == label_test)) / sum(weight_test)
    #prob_pred <- lable_pred
    tpr.fpr <- WeightedROC(random_forest_test_prep, label_test, weight_test)
    auc_rf <- WeightedAUC(tpr.fpr)
    cat("The AUC of model after reweighting: RF", "is", auc_rf, ".\n")
    cat("The accuracy of model: Random Forest on imbalanced testing data", "is", accu_rf_test*100, "%.\n")
    cat("The accuracy of model: Random Forest on balanced testing data", "is", accu_rf*100, "%.\n")
    cat("Time for training model Random Forest = ", time.rf.train[1], "s \n")
    cat("Time for testing model Random Forest = ",time.rf.test[1], "s \n")
    }
    
    #Train 1500
    if(tune.random.forest){
    time.rf.train <- system.time(random_forest_fit_1500 <- random_forest_train_1500(dat_train_balanced_rose,mtry = 154))
    save(random_forest_fit_1500, file = "../output/rf_train_1500_trees.RData")
    }
    #Test 1500
    random_forest_test_prep=NA
    if(tune.random.forest){
     load(file="../output/rf_train_1500_trees.RData")
     time.rf.test <- system.time(
       random_forest_test_prep <- random_forest_test(
         model = random_forest_fit_1500,testset = dat_test)
                   )
    
    random_forest_test_prep <- as.numeric(as.character(random_forest_test_prep))
    accu_rf_test <- mean(random_forest_test_prep == dat_test$label)
    random_forest_label<-round(random_forest_test_prep)
    accu_rf <- sum(weight_test * (random_forest_label == label_test)) / sum(weight_test)
    #prob_pred <- lable_pred
    tpr.fpr <- WeightedROC(random_forest_test_prep, label_test, weight_test)
    auc_rf <- WeightedAUC(tpr.fpr)
    cat("The AUC of model after reweighting: RF", "is", auc_rf, ".\n")
    cat("The accuracy of model: Random Forest on imbalanced testing data", "is", accu_rf_test*100, "%.\n")
    cat("The accuracy of model: Random Forest on balanced testing data", "is", accu_rf*100, "%.\n")
    cat("Time for training model Random Forest = ", time.rf.train[1], "s \n")
    cat("Time for testing model Random Forest = ",time.rf.test[1], "s \n")
    }
    
    #Train 2000
    if(tune.random.forest){
    time.rf.train <- system.time(random_forest_fit_2000 <- random_forest_train_2000(dat_train_balanced_rose,mtry = 154))
    save(random_forest_fit_2000, file = "../output/rf_train_2000_trees.RData")
    }
    #Test 2000
    random_forest_test_prep=NA
    if(tune.random.forest){
     load(file="../output/rf_train_2000_trees.RData")
     time.rf.test <- system.time(
       random_forest_test_prep <- random_forest_test(
         model = random_forest_fit_2000,testset = dat_test)
                   )
    
    random_forest_test_prep <- as.numeric(as.character(random_forest_test_prep))
    accu_rf_test <- mean(random_forest_test_prep == dat_test$label)
    random_forest_label<-round(random_forest_test_prep)
    accu_rf <- sum(weight_test * (random_forest_label == label_test)) / sum(weight_test)
    #prob_pred <- lable_pred
    tpr.fpr <- WeightedROC(random_forest_test_prep, label_test, weight_test)
    auc_rf <- WeightedAUC(tpr.fpr)
    cat("The AUC of model after reweighting: RF", "is", auc_rf, ".\n")
    cat("The accuracy of model: Random Forest on imbalanced testing data", "is", accu_rf_test*100, "%.\n")
    cat("The accuracy of model: Random Forest on balanced testing data", "is", accu_rf*100, "%.\n")
    cat("Time for training model Random Forest = ", time.rf.train[1], "s \n")
    cat("Time for testing model Random Forest = ",time.rf.test[1], "s \n")
    }
    #Train 2500
    if(tune.random.forest){
    time.rf.train <- system.time(random_forest_fit_2500 <- random_forest_train_2500(dat_train_balanced_rose,mtry = 154))
    save(random_forest_fit_2500, file = "../output/rf_train_2500_trees.RData")
    }
    #Test 2500
    random_forest_test_prep=NA
    if(tune.random.forest){
     load(file="../output/rf_train_2500_trees.RData")
     time.rf.test <- system.time(
       random_forest_test_prep <- random_forest_test(
         model = random_forest_fit_2500,testset = dat_test)
                   )
    
    random_forest_test_prep <- as.numeric(as.character(random_forest_test_prep))
    accu_rf_test <- mean(random_forest_test_prep == dat_test$label)
    random_forest_label<-round(random_forest_test_prep)
    accu_rf <- sum(weight_test * (random_forest_label == label_test)) / sum(weight_test)
    #prob_pred <- lable_pred
    tpr.fpr <- WeightedROC(random_forest_test_prep, label_test, weight_test)
    auc_rf <- WeightedAUC(tpr.fpr)
    cat("The AUC of model after reweighting: RF", "is", auc_rf, ".\n")
    cat("The accuracy of model: Random Forest on imbalanced testing data", "is", accu_rf_test*100, "%.\n")
    cat("The accuracy of model: Random Forest on balanced testing data", "is", accu_rf*100, "%.\n")
    cat("Time for training model Random Forest = ", time.rf.train[1], "s \n")
    cat("Time for testing model Random Forest = ",time.rf.test[1], "s \n")
    }
    
    <<<<<<< HEAD =======

    Testing Result: When trees = 500: The AUC of model after reweighting: RF is 0.5116745 . The accuracy of model: Random Forest on imbalanced testing data is 80.66667 %. The accuracy of model: Random Forest on balanced testing data is 51.16745 %. Time for training model Random Forest = 713.63 s Time for testing model Random Forest = 0.19 s

    When trees = 1000 The AUC of model after reweighting: RF is 0.5201491 . The accuracy of model: Random Forest on imbalanced testing data is 81 %. The accuracy of model: Random Forest on balanced testing data is 52.01491 %. Time for training model Random Forest = 1367.94 s Time for testing model Random Forest = 0.28 s

    When trees = 1500 The AUC of model after reweighting: RF is 0.5201491 . The accuracy of model: Random Forest on imbalanced testing data is 81 %. The accuracy of model: Random Forest on balanced testing data is 52.01491 %. Time for training model Random Forest = 2077.56 s Time for testing model Random Forest = 0.36 s

    When trees = 2000 The AUC of model after reweighting: RF is 0.5201491 . The accuracy of model: Random Forest on imbalanced testing data is 81 %. The accuracy of model: Random Forest on balanced testing data is 52.01491 %. Time for training model Random Forest = 3142.77 s Time for testing model Random Forest = 0.56 s

    When trees = 2500 The AUC of model after reweighting: RF is 0.5159118 . The accuracy of model: Random Forest on imbalanced testing data is 80.83333 %. The accuracy of model: Random Forest on balanced testing data is 51.59118 %. Time for training model Random Forest = 3963.67 s Time for testing model Random Forest = 0.62 s

    Therefore, we should use trees = 1000.

    Train RF with tuning parameters:

    source("../lib/random_forest.R")
    if(train.random.forest){
      time.rf.train <- system.time(random_forest_fit <- random_forest_train(dat_train_balanced_rose,mtry = 154))
      save(random_forest_fit, file = "../output/random_forest_train.RData")
      save(time.rf.train,file = "../output/random_forest_train_time.RData")
    }else{
      load(file = "../output/random_forest_train_time.RData")
      load(file = "../output/random_forest_train.RData")
    }

    Test RF with tuning parameters

    random_forest_test_prep=NA
    if(run.test){
     load(file="../output/random_forest_train.RData")
     time.rf.test <- system.time(
       random_forest_test_prep <- random_forest_test(
         model = random_forest_fit,testset = dat_test)
                   )
    }
    ## reweight the test data to represent a balanced label distribution
    if (run.gbm){
      accu <- mean(dat_test$label == label_pred)
      cat("The accuracy of GBM baseline model is", mean(dat_test$label == label_pred)*100, "%.\n")
      
    } else {
      label_test <- as.integer(dat_test$label)
      weight_test <- rep(NA, length(label_test))
      for (v in unique(label_test)){
        weight_test[label_test == v] = 0.5 * length(label_test) / length(label_test[label_test == v])
      }
      
      accu <- sum(weight_test * (label_pred == label_test)) / sum(weight_test)
      tpr.fpr <- WeightedROC(prob_pred, label_test, weight_test)
      auc <- WeightedAUC(tpr.fpr)
      
      
      cat("The accuracy of model:", model_labels[which.min(res_cv$mean_error)], "is", accu*100, "%.\n")
      cat("The AUC of model:", model_labels[which.min(res_cv$mean_error)], "is", auc, ".\n")
    }
    random_forest_test_prep <- as.numeric(as.character(random_forest_test_prep))
    accu_rf_test <- mean(random_forest_test_prep == dat_test$label)
    >>>>>>> 8034bda1773732b313f41b08d1963b46d0546d83

    Testing Result: When trees = 500: The AUC of model after reweighting: RF is 0.5116745 . The accuracy of model: Random Forest on imbalanced testing data is 80.66667 %. The accuracy of model: Random Forest on balanced testing data is 51.16745 %. Time for training model Random Forest = 713.63 s Time for testing model Random Forest = 0.19 s

    When trees = 1000 The AUC of model after reweighting: RF is 0.5201491 . The accuracy of model: Random Forest on imbalanced testing data is 81 %. The accuracy of model: Random Forest on balanced testing data is 52.01491 %. Time for training model Random Forest = 1367.94 s Time for testing model Random Forest = 0.28 s

    When trees = 1500 The AUC of model after reweighting: RF is 0.5201491 . The accuracy of model: Random Forest on imbalanced testing data is 81 %. The accuracy of model: Random Forest on balanced testing data is 52.01491 %. Time for training model Random Forest = 2077.56 s Time for testing model Random Forest = 0.36 s

    When trees = 2000 The AUC of model after reweighting: RF is 0.5201491 . The accuracy of model: Random Forest on imbalanced testing data is 81 %. The accuracy of model: Random Forest on balanced testing data is 52.01491 %. Time for training model Random Forest = 3142.77 s Time for testing model Random Forest = 0.56 s

    When trees = 2500 The AUC of model after reweighting: RF is 0.5159118 . The accuracy of model: Random Forest on imbalanced testing data is 80.83333 %. The accuracy of model: Random Forest on balanced testing data is 51.59118 %. Time for training model Random Forest = 3963.67 s Time for testing model Random Forest = 0.62 s

    Therefore, we should use trees = 1000.

    <<<<<<< HEAD

    Train RF with tuning parameters:

    source("../lib/random_forest.R")
    if(train.random.forest){
      time.rf.train <- system.time(random_forest_fit <- random_forest_train(dat_train_balanced_rose,mtry = 154))
      save(random_forest_fit, file = "../output/random_forest_train.RData")
      save(time.rf.train,file = "../output/random_forest_train_time.RData")
    }else{
      load(file = "../output/random_forest_train_time.RData")
      load(file = "../output/random_forest_train.RData")
    }

    Test RF with tuning parameters

    random_forest_test_prep=NA
    if(run.test){
     load(file="../output/random_forest_train.RData")
     time.rf.test <- system.time(
       random_forest_test_prep <- random_forest_test(
         model = random_forest_fit,testset = dat_test)
                   )
    }
    ## reweight the test data to represent a balanced label distribution
    if (run.gbm){
      accu <- mean(dat_test$label == label_pred)
      cat("The accuracy of GBM baseline model is", mean(dat_test$label == label_pred)*100, "%.\n")
      
    } else {
      label_test <- as.integer(dat_test$label)
      weight_test <- rep(NA, length(label_test))
      for (v in unique(label_test)){
        weight_test[label_test == v] = 0.5 * length(label_test) / length(label_test[label_test == v])
      }
      
      accu <- sum(weight_test * (label_pred == label_test)) / sum(weight_test)
      tpr.fpr <- WeightedROC(prob_pred, label_test, weight_test)
      auc <- WeightedAUC(tpr.fpr)
      
      
      cat("The accuracy of model:", model_labels[which.min(res_cv$mean_error)], "is", accu*100, "%.\n")
      cat("The AUC of model:", model_labels[which.min(res_cv$mean_error)], "is", auc, ".\n")
    }
    random_forest_test_prep <- as.numeric(as.character(random_forest_test_prep))
    accu_rf_test <- mean(random_forest_test_prep == dat_test$label)
    ======= >>>>>>> 8034bda1773732b313f41b08d1963b46d0546d83

    Calculate weightedAUC on testing split

    random_forest_label<-round(random_forest_test_prep)
    #prob_pred <- lable_pred
    tpr.fpr <- WeightedROC(random_forest_test_prep, label_test, weight_test)
    auc_rf <- WeightedAUC(tpr.fpr)

    Summary of RF

    cat("The AUC of model after reweighting: RF", "is", auc_rf, ".\n")
    cat("The accuracy of model: Random Forest on testing data", "is", accu_rf_test*100, "%.\n")
    cat("Time for training model Random Forest = ", time.rf.train[1], "s \n")
    cat("Time for testing model Random Forest = ",time.rf.test[1], "s \n")
    #label_test
    
    
    cat("The accuracy of model:", model_labels[which.min(res_cv$mean_error)], "is", accu*100, "%.\n")
    cat("The AUC of model:", model_labels[which.min(res_cv$mean_error)], "is", auc, ".\n")

    Summarize Running Time

    Prediction performance matters, so does the running times for constructing features and for training the model, especially when the computation resource is limited.

    # cat("Time for constructing training features=", tm_feature_train[1], "s \n")
    # cat("Time for constructing testing features=", tm_feature_test[1], "s \n")
    # cat("Time for training model=", tm_train[1], "s \n") 
    # cat("Time for testing model=", tm_test[1], "s \n")

    SVM Model

    • Balance the Training Data
    library(ROSE)
    if(run.svm){
      tm_svm_rebalanced_train <- NA
      if(needs.balanced){
        tm_svm_rebalanced_train <- system.time(svm_training_data <- ROSE(label ~ ., data = dat_train)$data)
        save(svm_training_data, file="../output/svm_training_data.RData")
        save(tm_svm_rebalanced_train, file="../output/tm_svm_rebalanced_train.RData")
      } else {
        svm_training_data <- dat_train
        tm_svm_rebalanced_train <- tm_feature_train
      }
    } else {
      load(file="../output/tm_svm_rebalanced_train.RData")
    }
    
    • Model Selection
    library(e1071)
    if(run.svm){
      tm_svm_linear_mod <- NA
      tm_svm_radial_mod <- NA
      
      if(model.selection){
        svm_model_auc <- rep(NA, 2)
        
        ### linear kernel
        if(run.cv){
          #best.linear.cost <- svm_linear_cost_tune(svm_training_data)
          #cat("The best cost for svm model with linear kernel is: ", best.linear.cost$best.parameters$cost)
          tm_svm_linear_mod <- system.time(svm_linear_mod <- svm_linear_train(svm_training_data, 0.01, K))
          save(svm_linear_mod, file="../output/svm_linear_mod.RData")
          save(tm_svm_linear_mod, file="../output/tm_svm_linear_mod.RData")
        } else {
          load(file="../output/svm_linear_mod.RData")
          load(file="../output/tm_svm_linear_mod.RData")
        }
        svm_linear_pred <- svm_test(svm_linear_mod, svm_training_data, TRUE)
        #mean(round(svm_linear_pred == svm_training_data$label))
        svm_linear_accu <- mean(round(svm_linear_pred == svm_training_data$label))
        tpr.fpr_linear <- WeightedROC(as.numeric(svm_linear_pred), svm_training_data$label)
        svm_model_auc[1] <- WeightedAUC(tpr.fpr_linear)
        
        
        ### radial basis kernel
        if(run.cv){
          #best.radial.cost <- svm_radial_cost_tune(svm_training_data)
          #radial_cost = best.radial.cost$best.parameters$cost
          #radial_gamma = best.radial.cost$best.parameters$gamma
          
          tm_svm_radial_mod < system.time(svm_radial_mod <- svm_radial_train(svm_training_data, 1, K))
          save(svm_radial_mod, file="../output/svm_radial_mod.RData")
          save(tm_svm_radial_mod, file="../output/tm_svm_radial_mod.RData")
        } else { 
          load(file="../output/svm_radial_mod.RData")
          load(file="../output/tm_svm_radial_mod.RData")
        }
        svm_radial_pred <- svm_test(svm_radial_mod, svm_training_data, TRUE)
        # evaluate performance
        svm_radial_accu <- mean(round(svm_radial_pred == svm_training_data$label))
        tpr.fpr_default <- WeightedROC(as.numeric(svm_radial_pred), svm_training_data$label)
        svm_model_auc[2] <- WeightedAUC(tpr.fpr_default)
      } else {
        load(file="../output/svm_linear_mod.RData")
        load(file="../output/tm_svm_linear_mod.RData")
        load(file="../output/svm_radial_mod.RData")
        load(file="../output/tm_svm_radial_mod.RData")
      }
      
      
      ### Evaluation on Testing Data
      tm_svm_rebalanced_test <- NA
      if(needs.balanced){
        tm_svm_rebalanced_test <- system.time(svm_testing_data <- ROSE(label ~ ., data = dat_test)$data)
        save(svm_testing_data, file="../output/svm_testing_data.RData")
        save(tm_svm_rebalanced_test, file="../output/tm_svm_rebalanced_test.RData")
      } else {
        svm_testing_data <- dat_test
        tm_svm_rebalanced_test <- tm_feature_test
      }
      
      if(run.svm.test){
        svm_auc <- rep(NA, 2)
        svm_accu <- rep(NA, 2)
        ## linear
        tm_svm_linear_test <- system.time(svm_linear_pred <- svm_test(svm_linear_mod, svm_testing_data))
        svm_accu[1] = mean(round(svm_linear_pred == svm_testing_data$label))
        tpr.fpr.linear <- WeightedROC(as.numeric(svm_linear_pred), svm_testing_data$label)
        svm_auc[1] = WeightedAUC(tpr.fpr.linear)
        ## rbf
        tm_svm_rbf_test <- system.time(svm_rbf_pred <- svm_test(svm_radial_mod, svm_testing_data))
        svm_accu[2] = mean(round(svm_rbf_pred == svm_testing_data$label))
        tpr.fpr.rbf <- WeightedROC(as.numeric(svm_linear_pred), svm_testing_data$label)
        svm_auc[2] = WeightedAUC(tpr.fpr.rbf)
        
        save(tm_svm_radial_mod, file="../output/tm_svm_linear_test.RData")
        
        ## performance
        svm_auc
        svm_accu
    
        cat("The accuracy of svm model is", svm_accu[2]*100, "%.\n")
        cat("The AUC of svm model is", svm_auc[2], ".\n")
      } else {
        load(file="../output/tm_svm_rebalanced_test.RData")
      }
    } else {
      load(file="../output/svm_radial_mod.RData")
      load(file="../output/tm_svm_radial_mod.RData")
    }
      <<<<<<< HEAD =======
    • Evaluation on Testing Data
    tm_svm_rebalanced_test <- NA
    if(needs.balanced){
      tm_svm_rebalanced_test <- system.time(svm_testing_data <- ROSE(label ~ ., data = dat_test)$data)
      save(svm_testing_data, file="../output/svm_testing_data.RData")
    } else {
      load(file="../output/svm_testing_data.RData")
    }
    tm_svm_test <- system.time(svm_pred <- svm_test(svm_linear_mod, svm_testing_data))
    
    svm_accu = mean(round(svm_pred == svm_testing_data$label))
    tpr.fpr <- WeightedROC(as.numeric(svm_pred), svm_testing_data$label)
    svm_auc = WeightedAUC(tpr.fpr)
    
    cat("The accuracy of svm model is", svm_accu*100, "%.\n")
    cat("The AUC of svm model is", svm_auc, ".\n")
      >>>>>>> 8034bda1773732b313f41b08d1963b46d0546d83
    • Summarize Running Time
    <<<<<<< HEAD
    #cat("Time for rebalancing training data =", tm_svm_rebalanced_train[1], "s \n")
    #cat("Time for rebalancing testing data =", tm_svm_rebalanced_test[1], "s \n")
    cat("Time for training svm model =", tm_svm_radial_mod[1], "s \n")
    
    Time for training svm model = NA s 
    =======
    cat("Time for rebalancing training data =", tm_svm_rebalanced_train[1], "s \n")
    cat("Time for rebalancing testing data =", tm_svm_rebalanced_test[1], "s \n")
    cat("Time for training model =", tm_svm_linear_mod[1], "s \n")
    cat("Time for testing model=", tm_svm_test[1], "s \n")

    ridge model

    apply constructed ridge model to the training data

    tm_ridge_train <- NA
    if (train.ridge){
      dat_train_rebalanced <- ROSE(label ~ ., data = dat_train, seed=2021)$data
      tm_ridge_train <- system.time(ridge_cv_model<-ridge_train(train_data=dat_train_rebalanced, alpha=alpha, K=K, lambda=lambda))
      save(ridge_cv_model, file="../output/ridge_cv_model.RData")
      save(tm_ridge_train, file="../output/ridge_train_time.RData")
    }else{
      load(file="../output/ridge_cv_model.RData")
      load(file="../output/ridge_train_time.RData")
    }

    use cross-validation to choose the optimal lambda with smallest MSE

    if (run.cv){
      set.seed(2020)
      feature_train = as.matrix(dat_train[, -6007])
      label_train = as.integer(dat_train$label)
      ridge_model = cv.glmnet(x=feature_train, y=label_train, alpha=alpha, nfolds=K, lambda=lambda)
      opt_lambda = ridge_model$lambda.min
      save(opt_lambda, file="../output/ridge_optimal_lambda.RData")
    }else{
      load(file="../output/ridge_optimal_lambda.RData")
    }

    predict testing data with the optimal lambda

    tm_ridge_test = NA
    if(run.test){
      load("../output/ridge_cv_model.RData")
      feature_test <- as.matrix(dat_test[, -6007])
      tm_ridge_test <- system.time(label_pred<-as.integer(ridge_test(model=ridge_cv_model, features=feature_test, pred.type = 'class')))
      save(tm_ridge_test, file="../output/ridge_test_time.RData")
    } else{
      load(file="../output/ridge_test_time.RData")
    }

    summarize running time

    cat("Time for constructing training features=", tm_feature_train[1], "s \n")
    cat("Time for constructing testing features=", tm_feature_test[1], "s \n")
    cat("Time for training ridge model=", tm_ridge_train[1], "s \n") 
    cat("Time for testing ridge model=", tm_ridge_test[1], "s \n")

    run evaluation on independent testing data

    load("../output/ridge_cv_model.RData")
    feature_test <- as.matrix(dat_test[, -6007])
    label_pred = as.integer(predict(ridge_cv_model, s=opt_lambda, newx=feature_test, type='class'))
    label_test = as.integer(dat_test$label)
    compare <- cbind (label_test, label_pred)
    ridge_accuracy = mean(apply(compare, 1, min)/apply(compare, 1, max)) 
    cat("The accuracy of the ridge model is", ridge_accuracy*100, "%.\n")
    ridge_AUC = auc(roc(label_pred,label_test))
    cat("The AUC of the ridge model is", ridge_AUC, ".\n")
    >>>>>>> 8034bda1773732b313f41b08d1963b46d0546d83

    ###Reference - Du, S., Tao, Y., & Martinez, A. M. (2014). Compound facial expressions of emotion. Proceedings of the National Academy of Sciences, 111(15), E1454-E1462.

    <<<<<<< HEAD
    ---
title: "Main"
author: "Chengliang Tang, Yujie Wang, Diane Lu, Tian Zheng"
output:
  html_document:
    df_print: paged
  html_notebook: default
  pdf_document: default
---

In your final repo, there should be an R markdown file that organizes **all computational steps** for evaluating your proposed Facial Expression Recognition framework. 

This file is currently a template for running evaluation experiments. You should update it according to your codes but following precisely the same structure. 

```{r message=FALSE}
#Test Branch created
if(!require("EBImage")){
 install.packages("BiocManager")
 BiocManager::install("EBImage")
}
if(!require("R.matlab")){
  install.packages("R.matlab")
}
if(!require("readxl")){
  install.packages("readxl")
}

if(!require("dplyr")){
  install.packages("dplyr")
}
if(!require("readxl")){
  install.packages("readxl")
}

if(!require("ggplot2")){
  install.packages("ggplot2")
}

if(!require("caret")){
  install.packages("caret")
}

if(!require("glmnet")){
  install.packages("glmnet")
}

if(!require("WeightedROC")){
  install.packages("WeightedROC")
}

if(!require("gbm")){
  install.packages("gbm")
}

if(!require("DMwR")){
  install.packages("DMwR")
}

library(R.matlab)
library(readxl)
library(dplyr)
#library(EBImage)
library(ggplot2)
library(caret)
library(glmnet)
library(WeightedROC)
library(gbm)
library(DMwR)
```
New libraries
```{r message=FALSE}
if(!require("randomForest")){
 install.packages("randomForest")
}
if(!require("xgboost")){
 install.packages("xgboost")
}
if(!require("tibble")){
 install.packages("tibble")
}
if(!require("ROSE")){
 install.packages("ROSE")
}
if(!require("ggplot2")){
 install.packages("ggplot2")
}
if(!require("tidyverse")){
 install.packages("tidyverse")
}

if(!require("AUC")){
 install.packages("AUC")
}
if(!require("e1071")){
 install.packages("e1071")
}
if(!require("OpenImageR")){
 install.packages("OpenImageR")
}
if(!require("caTools")){
  install.packages("caTools")
}
library(OpenImageR)
library(AUC)
library(e1071)
library(randomForest)
library(xgboost)
library(tibble)
library(ROSE)
library(ggplot2)
library(tidyverse)
library(AUC)
library(e1071)
library(caTools)
```


### Step 0 set work directories
```{r wkdir, eval=FALSE}
set.seed(2020)
setwd("../doc")
# here replace it with your own path or manually set it in RStudio to where this rmd file is located. 
# use relative path for reproducibility
```

Provide directories for training images. Training images and Training fiducial points will be in different subfolders. 
```{r}
train_dir <- "../data/train_set/" # This will be modified for different data sets.
train_image_dir <- paste(train_dir, "images/", sep="")
train_pt_dir <- paste(train_dir,  "points/", sep="")
train_label_path <- paste(train_dir, "label.csv", sep="")
```

### Step 1: set up controls for evaluation experiments.

In this chunk, we have a set of controls for the evaluation experiments. 

+ (T/F) cross-validation on the training set
+ (T/F) reweighting the samples for training set 
+ (number) K, the number of CV folds
+ (number) gbm.numtrees, the number of trees to use in GBM baseline
+ (T/F) process features for training set
+ (T/F) run evaluation on an independent test set
+ (T/F) process features for test set
+ (T/F) return polynomial features matrix only
+ (T/F) add polynomial features to starter code features matrix
+ (T/F) run gbm baseline model

```{r exp_setup}
run.cv <- TRUE # run cross-validation on the training set
sample.reweight <- FALSE # run sample reweighting in model training
K <- 5  # number of CV folds
run.feature.train <- TRUE # process features for training set
run.test <- TRUE # run evaluation on an independent test set

# gbm
gbm.numtrees <- 1000 #number of trees to use in gbm
run.feature.test <- TRUE # process features for test set
run.poly.feature <- TRUE # process poly features
run.add.poly.feature <- TRUE # and poly features to dist matrix
run.gbm <- TRUE

# svm
run.svm <- FALSE # svm is the chosen advanced model
needs.balanced <- TRUE # balance data for model fitting
model.selection <- TRUE # perform model selection on svm models
run.svm.test <- TRUE # evaluate performance on the test set

# random forest
run.balanced.data <- TRUE # Whether or not balance the data
train.random.forest <- F # Train Random Forest Model
tune.random.forest <- F # Tune Random Forest Model
```

Using cross-validation or independent test set evaluation, we compare the performance of models with different specifications. In this Starter Code, we tune parameter lambda (the amount of shrinkage) for logistic regression with LASSO penalty.

```{r model_setup}
lmbd = c(1e-3, 5e-3, 1e-2, 5e-2, 1e-1)
model_labels = paste("LASSO Penalty with lambda =", lmbd)
```

### Step 2: import data and train-test split 
```{r}
#train-test split
info <- read.csv(train_label_path)
n <- nrow(info) #get number of rows from csv
n_train <- round(n*(4/5), 0) #use 4/5 amount of data for training
train_idx <- sample(info$Index, n_train, replace = F) #grab indexs used for training
test_idx <- setdiff(info$Index, train_idx) # get indexs not used for training
```

If you choose to extract features from images, such as using Gabor filter, R memory will exhaust all images are read together. The solution is to repeat reading a smaller batch(e.g 100) and process them. 
```{r}
n_files <- length(list.files(train_image_dir,'*jpg'))

# image_list <- list()
# for(i in 1:100){
#    image_list[[i]] <- readImage(paste0(train_image_dir, sprintf("%04d", i), ".jpg"))
# }
```

Fiducial points are stored in matlab format. In this step, we read them and store them in a list.
```{r read fiducial points}
#function to read fiducial points
#input: index
#output: matrix of fiducial points corresponding to the index
readMat.matrix <- function(index){
     return(round(readMat(paste0(train_pt_dir, sprintf("%04d", index), ".mat"))[[1]],0))
}

#load fiducial points
fiducial_pt_list <- lapply(1:n_files, readMat.matrix)
save(fiducial_pt_list, file="../output/fiducial_pt_list.RData")
```

### Step 3: construct features and responses

+ The follow plots show how pairwise distance between fiducial points can work as feature for facial emotion recognition.

  + In the first column, 78 fiducials points of each emotion are marked in order. 
  + In the second column distributions of vertical distance between right pupil(1) and  right brow peak(21) are shown in  histograms. For example, the distance of an angry face tends to be shorter than that of a surprised face.
  + The third column is the distributions of vertical distances between right mouth corner(50)
and the midpoint of the upper lip(52).  For example, the distance of an happy face tends to be shorter than that of a sad face.

![Figure1](../figs/feature_visualization.jpg)

`feature.R` should be the wrapper for all your feature engineering functions and options. The function `feature( )` should have options that correspond to different scenarios for your project and produces an R object that contains features and responses that are required by all the models you are going to evaluate later. 
  
  + `feature.R`
  + Input: list of images or fiducial point
  + Output: an RData file that contains extracted features and corresponding responses

```{r feature}
source("../lib/feature.R")
tm_feature_train <- NA
if(run.feature.train){
  tm_feature_train <- system.time(dat_train<-feature(fiducial_pt_list,train_idx, run.poly.feature, run.add.poly.feature))
  save(dat_train, file="../output/feature_train.RData")
}else{
  load(file="../output/feature_train.RData")
}
tm_feature_test <- NA
if(run.feature.test){
  tm_feature_test <- system.time(dat_test <- feature(fiducial_pt_list, test_idx, run.poly.feature, run.add.poly.feature))
  save(dat_test, file="../output/feature_test.RData")
}else{
  load(file="../output/feature_test.RData")
}
# transfer label column from factor to numeric
dat_train$label <- as.numeric(dat_train$label)-1
dat_test$label <- as.numeric(dat_test$label)-1
#Rebalancing training data-Bootstrap Random Over-Sampling Examples Technique (ROSE) source
if(run.balanced.data){
dat_train_balanced_rose<-ROSE(label~., dat_train,seed=2020)$data
save(dat_train_balanced_rose, file="../output/balanced_data.RData")
}else{
  load(file="../output/balanced_data.RData")
}
table(dat_train_balanced_rose$label)
```

### Step 4: Train a classification model with training features and responses
Call the train model and test model from library. 

`train.R` and `test.R` should be wrappers for all your model training steps and your classification/prediction steps. 

+ `train.R`
  + Input: a data frame containing features and labels and a parameter list.
  + Output:a trained model
+ `test.R`
  + Input: the fitted classification model using training data and processed features from testing images 
  + Input: an R object that contains a trained classifier.
  + Output: training model specification

+ In this Starter Code, we use logistic regression with LASSO penalty to do classification. 

```{r loadlib}
source("../lib/train.R") 
source("../lib/test.R")
```

#### Model selection with cross-validation
* Do model selection by choosing among different values of training model parameters.

```{r runcv}
source("../lib/cross_validation.R")
feature_train = as.matrix(dat_train[, 1:ncol(dat_train)-1])
label_train = as.integer(dat_train$label)
if(run.cv){
  res_cv <- matrix(0, nrow = length(lmbd), ncol = 4)
  for(i in 1:length(lmbd)){
    cat("lambda = ", lmbd[i], "\n")
    res_cv[i,] <- cv.function(features = feature_train, labels = label_train, K, 
                              l = lmbd[i], reweight = sample.reweight)
  save(res_cv, file="../output/res_cv.RData")
  }
}else{
  load("../output/res_cv.RData")
}
```


Visualize cross-validation results.
```{r cv_vis}
# res_cv_rf  <- as.data.frame(res_cv_rf )
# colnames(res_cv_rf ) <- c("mean_error", "sd_error", "mean_AUC", "sd_AUC")
# res_cv_rf$k = as.factor(lmbd)
# 
# if(run.cv){
#   p1 <- res_cv_rf  %>%
#     ggplot(aes(x = as.factor(lmbd), y = mean_error,
#                ymin = mean_error - sd_error, ymax = mean_error + sd_error)) +
#     geom_crossbar() +
#     theme(axis.text.x = element_text(angle = 90, hjust = 1))
# 
#   p2 <- res_cv_rf  %>%
#     ggplot(aes(x = as.factor(lmbd), y = mean_AUC,
#                ymin = mean_AUC - sd_AUC, ymax = mean_AUC + sd_AUC)) +
#     geom_crossbar() +
#     theme(axis.text.x = element_text(angle = 90, hjust = 1))
# 
#   print(p1)
#   print(p2)
# }
# lambda=0.01 is the best
```


* Choose the "best" parameter value
```{r best_model}
#par_best <- lmbd[which.min(res_cv_rf$mean_error)] # lmbd[which.max(res_cv$mean_AUC)]
```

# Advanced Models:
Create weight test
```{r}
label_test <- as.integer(dat_test$label)
weight_test <- rep(NA, length(label_test))
for (i in unique(label_test)){
  weight_test[label_test == i] = 0.5 * length(label_test) / length(label_test[label_test == i])
}
# training weights
weight_train <- rep(NA, length(label_train))
for (v in unique(label_train)){
  weight_train[label_train == v] = 0.5 * length(label_train) / length(label_train[label_train == v])
}

if (run.gbm){
  if (sample.reweight){
    tm_train <- system.time(fit_train <- train_gbm(dat_train, s=0.1, K=K, n=gbm.numtrees,w = weight_train))
  } else {
    tm_train <- system.time(fit_train <- train_gbm(dat_train, s=0.1, K=K, n=gbm.numtrees,w = NULL))
  }
  
  # plot the performance
  best.iter.oob <- gbm.perf(fit_train,method="OOB")  # returns out-of-bag estimated best number of trees
  print(best.iter.oob)
  best.iter.cv <- gbm.perf(fit_train,method="cv")   # returns K-fold cv estimate of best number of trees
  print(best.iter.cv)

} else {
  if (sample.reweight){
    tm_train <- system.time(fit_train <- train(feature_train, label_train, w = weight_train, par_best))
  } else {
    tm_train <- system.time(fit_train <- train(feature_train, label_train, w = NULL, par_best))
  }
}
save(fit_train, file="../output/fit_train.RData")
```



### Step 5: Run test on test images
```{r test}
tm_test = NA
feature_test <- as.matrix(dat_test[, 1:ncol(dat_test)-1])
if(run.test){
  load(file="../output/fit_train.RData")
  if (run.gbm){
    tm_test <- system.time(prob_pred<-test_gbm(fit_train,as.data.frame(feature_test),n=best.iter.cv,pred.type = 'response'))
    
    label_pred <- colnames(prob_pred)[apply(prob_pred, 1, which.max)]
    
  } else {
    tm_test <- system.time({label_pred <- as.integer(test(fit_train, feature_test, pred.type = 'class')); 
                            prob_pred <- test(fit_train, feature_test, pred.type = 'response')})  
  }
  
}
```

Random Forest:

## Tune RF
```{r}
source("../lib/random_forest.R")
if(tune.random.forest){
time.rf.tune <- system.time(rf.tune <- random_forest_tune(dat_train_balanced_rose))
save(rf.tune, file="../output/rf_tune.RData")
}else(
  load("../output/rf_tune.RData")
)
rf.tune
```
mtry = 154 is the best.

## Find the best ntrees
```{r}
source("../lib/random_forest.R")

#Train 500
if(tune.random.forest){
time.rf.train <- system.time(random_forest_fit_500 <- random_forest_train_500(dat_train_balanced_rose,mtry = 154))
save(random_forest_fit_500, file = "../output/rf_train_500_trees.RData")
}
#Test 500
random_forest_test_prep=NA
if(tune.random.forest){
 load(file="../output/rf_train_500_trees.RData")
 time.rf.test <- system.time(
   random_forest_test_prep <- random_forest_test(
     model = random_forest_fit_500,testset = dat_test)
               )

random_forest_test_prep <- as.numeric(as.character(random_forest_test_prep))
accu_rf_test <- mean(random_forest_test_prep == dat_test$label)
random_forest_label<-round(random_forest_test_prep)
accu_rf <- sum(weight_test * (random_forest_label == label_test)) / sum(weight_test)
#prob_pred <- lable_pred
tpr.fpr <- WeightedROC(random_forest_test_prep, label_test, weight_test)
auc_rf <- WeightedAUC(tpr.fpr)
cat("The AUC of model after reweighting: RF", "is", auc_rf, ".\n")
cat("The accuracy of model: Random Forest on imbalanced testing data", "is", accu_rf_test*100, "%.\n")
cat("The accuracy of model: Random Forest on balanced testing data", "is", accu_rf*100, "%.\n")
cat("Time for training model Random Forest = ", time.rf.train[1], "s \n")
cat("Time for testing model Random Forest = ",time.rf.test[1], "s \n")
}
# The AUC of model after reweighting: RF is 0.5031999 .
# The accuracy of model: Random Forest on imbalanced testing data is 80.33333 %.
# The accuracy of model: Random Forest on balanced testing data is 50.31999 %.
# Time for training model Random Forest =  20.95 s 
# Time for testing model Random Forest =  0.09 s 

#Train 1000
if(tune.random.forest){
time.rf.train <- system.time(random_forest_fit_1000 <- random_forest_train_1000(dat_train_balanced_rose,mtry = 154))
save(random_forest_fit_1000, file = "../output/rf_train_1000_trees.RData")
}
#Test 1000
random_forest_test_prep=NA
if(tune.random.forest){
 load(file="../output/rf_train_1000_trees.RData")
 time.rf.test <- system.time(
   random_forest_test_prep <- random_forest_test(
     model = random_forest_fit_1000,testset = dat_test)
               )

random_forest_test_prep <- as.numeric(as.character(random_forest_test_prep))
accu_rf_test <- mean(random_forest_test_prep == dat_test$label)
random_forest_label<-round(random_forest_test_prep)
accu_rf <- sum(weight_test * (random_forest_label == label_test)) / sum(weight_test)
#prob_pred <- lable_pred
tpr.fpr <- WeightedROC(random_forest_test_prep, label_test, weight_test)
auc_rf <- WeightedAUC(tpr.fpr)
cat("The AUC of model after reweighting: RF", "is", auc_rf, ".\n")
cat("The accuracy of model: Random Forest on imbalanced testing data", "is", accu_rf_test*100, "%.\n")
cat("The accuracy of model: Random Forest on balanced testing data", "is", accu_rf*100, "%.\n")
cat("Time for training model Random Forest = ", time.rf.train[1], "s \n")
cat("Time for testing model Random Forest = ",time.rf.test[1], "s \n")
}

#Train 1500
if(tune.random.forest){
time.rf.train <- system.time(random_forest_fit_1500 <- random_forest_train_1500(dat_train_balanced_rose,mtry = 154))
save(random_forest_fit_1500, file = "../output/rf_train_1500_trees.RData")
}
#Test 1500
random_forest_test_prep=NA
if(tune.random.forest){
 load(file="../output/rf_train_1500_trees.RData")
 time.rf.test <- system.time(
   random_forest_test_prep <- random_forest_test(
     model = random_forest_fit_1500,testset = dat_test)
               )

random_forest_test_prep <- as.numeric(as.character(random_forest_test_prep))
accu_rf_test <- mean(random_forest_test_prep == dat_test$label)
random_forest_label<-round(random_forest_test_prep)
accu_rf <- sum(weight_test * (random_forest_label == label_test)) / sum(weight_test)
#prob_pred <- lable_pred
tpr.fpr <- WeightedROC(random_forest_test_prep, label_test, weight_test)
auc_rf <- WeightedAUC(tpr.fpr)
cat("The AUC of model after reweighting: RF", "is", auc_rf, ".\n")
cat("The accuracy of model: Random Forest on imbalanced testing data", "is", accu_rf_test*100, "%.\n")
cat("The accuracy of model: Random Forest on balanced testing data", "is", accu_rf*100, "%.\n")
cat("Time for training model Random Forest = ", time.rf.train[1], "s \n")
cat("Time for testing model Random Forest = ",time.rf.test[1], "s \n")
}

#Train 2000
if(tune.random.forest){
time.rf.train <- system.time(random_forest_fit_2000 <- random_forest_train_2000(dat_train_balanced_rose,mtry = 154))
save(random_forest_fit_2000, file = "../output/rf_train_2000_trees.RData")
}
#Test 2000
random_forest_test_prep=NA
if(tune.random.forest){
 load(file="../output/rf_train_2000_trees.RData")
 time.rf.test <- system.time(
   random_forest_test_prep <- random_forest_test(
     model = random_forest_fit_2000,testset = dat_test)
               )

random_forest_test_prep <- as.numeric(as.character(random_forest_test_prep))
accu_rf_test <- mean(random_forest_test_prep == dat_test$label)
random_forest_label<-round(random_forest_test_prep)
accu_rf <- sum(weight_test * (random_forest_label == label_test)) / sum(weight_test)
#prob_pred <- lable_pred
tpr.fpr <- WeightedROC(random_forest_test_prep, label_test, weight_test)
auc_rf <- WeightedAUC(tpr.fpr)
cat("The AUC of model after reweighting: RF", "is", auc_rf, ".\n")
cat("The accuracy of model: Random Forest on imbalanced testing data", "is", accu_rf_test*100, "%.\n")
cat("The accuracy of model: Random Forest on balanced testing data", "is", accu_rf*100, "%.\n")
cat("Time for training model Random Forest = ", time.rf.train[1], "s \n")
cat("Time for testing model Random Forest = ",time.rf.test[1], "s \n")
}
#Train 2500
if(tune.random.forest){
time.rf.train <- system.time(random_forest_fit_2500 <- random_forest_train_2500(dat_train_balanced_rose,mtry = 154))
save(random_forest_fit_2500, file = "../output/rf_train_2500_trees.RData")
}
#Test 2500
random_forest_test_prep=NA
if(tune.random.forest){
 load(file="../output/rf_train_2500_trees.RData")
 time.rf.test <- system.time(
   random_forest_test_prep <- random_forest_test(
     model = random_forest_fit_2500,testset = dat_test)
               )

random_forest_test_prep <- as.numeric(as.character(random_forest_test_prep))
accu_rf_test <- mean(random_forest_test_prep == dat_test$label)
random_forest_label<-round(random_forest_test_prep)
accu_rf <- sum(weight_test * (random_forest_label == label_test)) / sum(weight_test)
#prob_pred <- lable_pred
tpr.fpr <- WeightedROC(random_forest_test_prep, label_test, weight_test)
auc_rf <- WeightedAUC(tpr.fpr)
cat("The AUC of model after reweighting: RF", "is", auc_rf, ".\n")
cat("The accuracy of model: Random Forest on imbalanced testing data", "is", accu_rf_test*100, "%.\n")
cat("The accuracy of model: Random Forest on balanced testing data", "is", accu_rf*100, "%.\n")
cat("Time for training model Random Forest = ", time.rf.train[1], "s \n")
cat("Time for testing model Random Forest = ",time.rf.test[1], "s \n")
}

```
Testing Result:
When trees = 500:
The AUC of model after reweighting: RF is 0.5116745 .
The accuracy of model: Random Forest on imbalanced testing data is 80.66667 %.
The accuracy of model: Random Forest on balanced testing data is 51.16745 %.
Time for training model Random Forest =  713.63 s 
Time for testing model Random Forest =  0.19 s 

When trees = 1000
The AUC of model after reweighting: RF is 0.5201491 .
The accuracy of model: Random Forest on imbalanced testing data is 81 %.
The accuracy of model: Random Forest on balanced testing data is 52.01491 %.
Time for training model Random Forest =  1367.94 s 
Time for testing model Random Forest =  0.28 s 

When trees = 1500
The AUC of model after reweighting: RF is 0.5201491 .
The accuracy of model: Random Forest on imbalanced testing data is 81 %.
The accuracy of model: Random Forest on balanced testing data is 52.01491 %.
Time for training model Random Forest =  2077.56 s 
Time for testing model Random Forest =  0.36 s 

When trees = 2000
The AUC of model after reweighting: RF is 0.5201491 .
The accuracy of model: Random Forest on imbalanced testing data is 81 %.
The accuracy of model: Random Forest on balanced testing data is 52.01491 %.
Time for training model Random Forest =  3142.77 s 
Time for testing model Random Forest =  0.56 s 

When trees = 2500
The AUC of model after reweighting: RF is 0.5159118 .
The accuracy of model: Random Forest on imbalanced testing data is 80.83333 %.
The accuracy of model: Random Forest on balanced testing data is 51.59118 %.
Time for training model Random Forest =  3963.67 s 
Time for testing model Random Forest =  0.62 s 

Therefore, we should use trees = 1000.

## Train RF with tuning parameters:
```{r}
source("../lib/random_forest.R")
if(train.random.forest){
  time.rf.train <- system.time(random_forest_fit <- random_forest_train(dat_train_balanced_rose,mtry = 154))
  save(random_forest_fit, file = "../output/random_forest_train.RData")
  save(time.rf.train,file = "../output/random_forest_train_time.RData")
}else{
  load(file = "../output/random_forest_train_time.RData")
  load(file = "../output/random_forest_train.RData")
}
```
## Test RF with tuning parameters
```{r}
random_forest_test_prep=NA
if(run.test){
 load(file="../output/random_forest_train.RData")
 time.rf.test <- system.time(
   random_forest_test_prep <- random_forest_test(
     model = random_forest_fit,testset = dat_test)
               )
}
## reweight the test data to represent a balanced label distribution
if (run.gbm){
  accu <- mean(dat_test$label == label_pred)
  cat("The accuracy of GBM baseline model is", mean(dat_test$label == label_pred)*100, "%.\n")
  
} else {
  label_test <- as.integer(dat_test$label)
  weight_test <- rep(NA, length(label_test))
  for (v in unique(label_test)){
    weight_test[label_test == v] = 0.5 * length(label_test) / length(label_test[label_test == v])
  }
  
  accu <- sum(weight_test * (label_pred == label_test)) / sum(weight_test)
  tpr.fpr <- WeightedROC(prob_pred, label_test, weight_test)
  auc <- WeightedAUC(tpr.fpr)
  
  
  cat("The accuracy of model:", model_labels[which.min(res_cv$mean_error)], "is", accu*100, "%.\n")
  cat("The AUC of model:", model_labels[which.min(res_cv$mean_error)], "is", auc, ".\n")
}
random_forest_test_prep <- as.numeric(as.character(random_forest_test_prep))
accu_rf_test <- mean(random_forest_test_prep == dat_test$label)
```
## Calculate weightedAUC on testing split
```{r}
random_forest_label<-round(random_forest_test_prep)
#prob_pred <- lable_pred
tpr.fpr <- WeightedROC(random_forest_test_prep, label_test, weight_test)
auc_rf <- WeightedAUC(tpr.fpr)
```
## Summary of RF
```{r}
cat("The AUC of model after reweighting: RF", "is", auc_rf, ".\n")
cat("The accuracy of model: Random Forest on testing data", "is", accu_rf_test*100, "%.\n")
cat("Time for training model Random Forest = ", time.rf.train[1], "s \n")
cat("Time for testing model Random Forest = ",time.rf.test[1], "s \n")
#label_test


cat("The accuracy of model:", model_labels[which.min(res_cv$mean_error)], "is", accu*100, "%.\n")
cat("The AUC of model:", model_labels[which.min(res_cv$mean_error)], "is", auc, ".\n")
```

### Summarize Running Time
Prediction performance matters, so does the running times for constructing features and for training the model, especially when the computation resource is limited. 
```{r running_time}
# cat("Time for constructing training features=", tm_feature_train[1], "s \n")
# cat("Time for constructing testing features=", tm_feature_test[1], "s \n")
# cat("Time for training model=", tm_train[1], "s \n") 
# cat("Time for testing model=", tm_test[1], "s \n")
```


### SVM Model

* Balance the Training Data

```{r}
library(ROSE)
if(run.svm){
  tm_svm_rebalanced_train <- NA
  if(needs.balanced){
    tm_svm_rebalanced_train <- system.time(svm_training_data <- ROSE(label ~ ., data = dat_train)$data)
    save(svm_training_data, file="../output/svm_training_data.RData")
    save(tm_svm_rebalanced_train, file="../output/tm_svm_rebalanced_train.RData")
  } else {
    svm_training_data <- dat_train
    tm_svm_rebalanced_train <- tm_feature_train
  }
} else {
  load(file="../output/tm_svm_rebalanced_train.RData")
}

```


* Model Selection

```{r}
library(e1071)
if(run.svm){
  tm_svm_linear_mod <- NA
  tm_svm_radial_mod <- NA
  
  if(model.selection){
    svm_model_auc <- rep(NA, 2)
    
    ### linear kernel
    if(run.cv){
      #best.linear.cost <- svm_linear_cost_tune(svm_training_data)
      #cat("The best cost for svm model with linear kernel is: ", best.linear.cost$best.parameters$cost)
      tm_svm_linear_mod <- system.time(svm_linear_mod <- svm_linear_train(svm_training_data, 0.01, K))
      save(svm_linear_mod, file="../output/svm_linear_mod.RData")
      save(tm_svm_linear_mod, file="../output/tm_svm_linear_mod.RData")
    } else {
      load(file="../output/svm_linear_mod.RData")
      load(file="../output/tm_svm_linear_mod.RData")
    }
    svm_linear_pred <- svm_test(svm_linear_mod, svm_training_data, TRUE)
    #mean(round(svm_linear_pred == svm_training_data$label))
    svm_linear_accu <- mean(round(svm_linear_pred == svm_training_data$label))
    tpr.fpr_linear <- WeightedROC(as.numeric(svm_linear_pred), svm_training_data$label)
    svm_model_auc[1] <- WeightedAUC(tpr.fpr_linear)
    
    
    ### radial basis kernel
    if(run.cv){
      #best.radial.cost <- svm_radial_cost_tune(svm_training_data)
      #radial_cost = best.radial.cost$best.parameters$cost
      #radial_gamma = best.radial.cost$best.parameters$gamma
      
      tm_svm_radial_mod < system.time(svm_radial_mod <- svm_radial_train(svm_training_data, 1, K))
      save(svm_radial_mod, file="../output/svm_radial_mod.RData")
      save(tm_svm_radial_mod, file="../output/tm_svm_radial_mod.RData")
    } else { 
      load(file="../output/svm_radial_mod.RData")
      load(file="../output/tm_svm_radial_mod.RData")
    }
    svm_radial_pred <- svm_test(svm_radial_mod, svm_training_data, TRUE)
    # evaluate performance
    svm_radial_accu <- mean(round(svm_radial_pred == svm_training_data$label))
    tpr.fpr_default <- WeightedROC(as.numeric(svm_radial_pred), svm_training_data$label)
    svm_model_auc[2] <- WeightedAUC(tpr.fpr_default)
  } else {
    load(file="../output/svm_linear_mod.RData")
    load(file="../output/tm_svm_linear_mod.RData")
    load(file="../output/svm_radial_mod.RData")
    load(file="../output/tm_svm_radial_mod.RData")
  }
  
  
  ### Evaluation on Testing Data
  tm_svm_rebalanced_test <- NA
  if(needs.balanced){
    tm_svm_rebalanced_test <- system.time(svm_testing_data <- ROSE(label ~ ., data = dat_test)$data)
    save(svm_testing_data, file="../output/svm_testing_data.RData")
    save(tm_svm_rebalanced_test, file="../output/tm_svm_rebalanced_test.RData")
  } else {
    svm_testing_data <- dat_test
    tm_svm_rebalanced_test <- tm_feature_test
  }
  
  if(run.svm.test){
    svm_auc <- rep(NA, 2)
    svm_accu <- rep(NA, 2)
    ## linear
    tm_svm_linear_test <- system.time(svm_linear_pred <- svm_test(svm_linear_mod, svm_testing_data))
    svm_accu[1] = mean(round(svm_linear_pred == svm_testing_data$label))
    tpr.fpr.linear <- WeightedROC(as.numeric(svm_linear_pred), svm_testing_data$label)
    svm_auc[1] = WeightedAUC(tpr.fpr.linear)
    ## rbf
    tm_svm_rbf_test <- system.time(svm_rbf_pred <- svm_test(svm_radial_mod, svm_testing_data))
    svm_accu[2] = mean(round(svm_rbf_pred == svm_testing_data$label))
    tpr.fpr.rbf <- WeightedROC(as.numeric(svm_linear_pred), svm_testing_data$label)
    svm_auc[2] = WeightedAUC(tpr.fpr.rbf)
    
    save(tm_svm_radial_mod, file="../output/tm_svm_linear_test.RData")
    
    ## performance
    svm_auc
    svm_accu

    cat("The accuracy of svm model is", svm_accu[2]*100, "%.\n")
    cat("The AUC of svm model is", svm_auc[2], ".\n")
  } else {
    load(file="../output/tm_svm_rebalanced_test.RData")
  }
} else {
  load(file="../output/svm_radial_mod.RData")
  load(file="../output/tm_svm_radial_mod.RData")
}
```

* Summarize Running Time

```{r}
#cat("Time for rebalancing training data =", tm_svm_rebalanced_train[1], "s \n")
#cat("Time for rebalancing testing data =", tm_svm_rebalanced_test[1], "s \n")
#cat("Time for training svm model =", tm_svm_radial_mod[1], "s \n")
cat("Time for testing svm model=", tm_svm_rbf_test[1], "s \n")
```


###Reference
- Du, S., Tao, Y., & Martinez, A. M. (2014). Compound facial expressions of emotion. Proceedings of the National Academy of Sciences, 111(15), E1454-E1462.
    =======
    ---
title: "Main"
author: "Chengliang Tang, Yujie Wang, Diane Lu, Tian Zheng"
output:
  html_document:
    df_print: paged
  html_notebook: default
  pdf_document: default
---

In your final repo, there should be an R markdown file that organizes **all computational steps** for evaluating your proposed Facial Expression Recognition framework. 

This file is currently a template for running evaluation experiments. You should update it according to your codes but following precisely the same structure. 

```{r message=FALSE}
#Test Branch created
if(!require("EBImage")){
 install.packages("BiocManager")
 BiocManager::install("EBImage")
}
if(!require("R.matlab")){
  install.packages("R.matlab")
}
if(!require("readxl")){
  install.packages("readxl")
}

if(!require("dplyr")){
  install.packages("dplyr")
}
if(!require("readxl")){
  install.packages("readxl")
}

if(!require("ggplot2")){
  install.packages("ggplot2")
}

if(!require("caret")){
  install.packages("caret")
}

if(!require("glmnet")){
  install.packages("glmnet")
}

if(!require("WeightedROC")){
  install.packages("WeightedROC")
}

if(!require("gbm")){
  install.packages("gbm")
}

if(!require("DMwR")){
  install.packages("DMwR")
}

library(R.matlab)
library(readxl)
library(dplyr)
#library(EBImage)
library(ggplot2)
library(caret)
library(glmnet)
library(WeightedROC)
library(gbm)
library(DMwR)
```
New libraries
```{r message=FALSE}
if(!require("randomForest")){
 install.packages("randomForest")
}
if(!require("xgboost")){
 install.packages("xgboost")
}
if(!require("tibble")){
 install.packages("tibble")
}
if(!require("ROSE")){
 install.packages("ROSE")
}
if(!require("ggplot2")){
 install.packages("ggplot2")
}
if(!require("tidyverse")){
 install.packages("tidyverse")
}

if(!require("AUC")){
 install.packages("AUC")
}
if(!require("e1071")){
 install.packages("e1071")
}
if(!require("OpenImageR")){
 install.packages("OpenImageR")
}
if(!require("caTools")){
  install.packages("caTools")
}
library(OpenImageR)
library(AUC)
library(e1071)
library(randomForest)
library(xgboost)
library(tibble)
library(ROSE)
library(ggplot2)
library(tidyverse)
library(AUC)
library(e1071)
library(caTools)
```

```{r}
if(!require("prediction")){
  install.packages("prediction")
}
if(!require("pROC")){
  install.packages("pROC")
}
library(prediction)
library(pROC)
```


### Step 0 set work directories
```{r wkdir, eval=FALSE}
set.seed(2020)
setwd("../doc")
# here replace it with your own path or manually set it in RStudio to where this rmd file is located. 
# use relative path for reproducibility
```

Provide directories for training images. Training images and Training fiducial points will be in different subfolders. 
```{r}
train_dir <- "../data/train_set/" # This will be modified for different data sets.
train_image_dir <- paste(train_dir, "images/", sep="")
train_pt_dir <- paste(train_dir,  "points/", sep="")
train_label_path <- paste(train_dir, "label.csv", sep="")
```

### Step 1: set up controls for evaluation experiments.

In this chunk, we have a set of controls for the evaluation experiments. 

+ (T/F) cross-validation on the training set
+ (T/F) reweighting the samples for training set 
+ (number) K, the number of CV folds
+ (number) gbm.numtrees, the number of trees to use in GBM baseline
+ (T/F) process features for training set
+ (T/F) run evaluation on an independent test set
+ (T/F) process features for test set
+ (T/F) return polynomial features matrix only
+ (T/F) add polynomial features to starter code features matrix
+ (T/F) run gbm baseline model
+ (0/1) alpha, alpha=0 for ridge regression, alpha=1 for lasso regression
+ (T/F) train ridge model

```{r exp_setup}
run.cv <- FALSE # run cross-validation on the training set
sample.reweight <- FALSE # run sample reweighting in model training
K <- 5  # number of CV folds
gbm.numtrees <- 1000 #number of trees to use in gbm
run.feature.train <- TRUE # process features for training set
run.test <- TRUE # run evaluation on an independent test set
run.feature.test <- TRUE # process features for test set
run.poly.feature <- TRUE # process poly features
run.add.poly.feature <- TRUE # and poly features to dist matrix
run.gbm <- TRUE
needs.balanced <- TRUE # balance data for model fitting
model.selection <- TRUE # perform model selection on svm models
run.balanced.data <- TRUE # Whether or not balance the data
train.random.forest <- F # Train Random Forest Model
tune.random.forest <- F # Tune Random Forest Model
alpha <- 0 # ridge regression
train.ridge <- TRUE # train ridge model
```

Using cross-validation or independent test set evaluation, we compare the performance of models with different specifications. In this Starter Code, we tune parameter lambda (the amount of shrinkage) for logistic regression with LASSO penalty.

```{r model_setup}
lmbd = c(1e-3, 5e-3, 1e-2, 5e-2, 1e-1)
model_labels = paste("LASSO Penalty with lambda =", lmbd)
```

The amount of the penalty for ridge regression can be fine-tuned using lambda.
```{r}
lambda = 10^seq(10, -2, length = 100)
```

### Step 2: import data and train-test split 
```{r}
#train-test split
info <- read.csv(train_label_path)
n <- nrow(info) #get number of rows from csv
n_train <- round(n*(4/5), 0) #use 4/5 amount of data for training
train_idx <- sample(info$Index, n_train, replace = F) #grab indexs used for training
test_idx <- setdiff(info$Index, train_idx) # get indexs not used for training
```

If you choose to extract features from images, such as using Gabor filter, R memory will exhaust all images are read together. The solution is to repeat reading a smaller batch(e.g 100) and process them. 
```{r}
n_files <- length(list.files(train_image_dir,'*jpg'))

# image_list <- list()
# for(i in 1:100){
#    image_list[[i]] <- readImage(paste0(train_image_dir, sprintf("%04d", i), ".jpg"))
# }
```

Fiducial points are stored in matlab format. In this step, we read them and store them in a list.
```{r read fiducial points}
#function to read fiducial points
#input: index
#output: matrix of fiducial points corresponding to the index
readMat.matrix <- function(index){
     return(round(readMat(paste0(train_pt_dir, sprintf("%04d", index), ".mat"))[[1]],0))
}

#load fiducial points
fiducial_pt_list <- lapply(1:n_files, readMat.matrix)
save(fiducial_pt_list, file="../output/fiducial_pt_list.RData")
```

### Step 3: construct features and responses

+ The follow plots show how pairwise distance between fiducial points can work as feature for facial emotion recognition.

  + In the first column, 78 fiducials points of each emotion are marked in order. 
  + In the second column distributions of vertical distance between right pupil(1) and  right brow peak(21) are shown in  histograms. For example, the distance of an angry face tends to be shorter than that of a surprised face.
  + The third column is the distributions of vertical distances between right mouth corner(50)
and the midpoint of the upper lip(52).  For example, the distance of an happy face tends to be shorter than that of a sad face.

![Figure1](../figs/feature_visualization.jpg)

`feature.R` should be the wrapper for all your feature engineering functions and options. The function `feature( )` should have options that correspond to different scenarios for your project and produces an R object that contains features and responses that are required by all the models you are going to evaluate later. 
  
  + `feature.R`
  + Input: list of images or fiducial point
  + Output: an RData file that contains extracted features and corresponding responses

```{r feature}
source("../lib/feature.R")
tm_feature_train <- NA
if(run.feature.train){
  tm_feature_train <- system.time(dat_train<-feature(fiducial_pt_list,train_idx, run.poly.feature, run.add.poly.feature))
  save(dat_train, file="../output/feature_train.RData")
}else{
  load(file="../output/feature_train.RData")
}
tm_feature_test <- NA
if(run.feature.test){
  tm_feature_test <- system.time(dat_test <- feature(fiducial_pt_list, test_idx, run.poly.feature, run.add.poly.feature))
  save(dat_test, file="../output/feature_test.RData")
}else{
  load(file="../output/feature_test.RData")
}
# transfer label column from factor to numeric
dat_train$label <- as.numeric(dat_train$label)-1
dat_test$label <- as.numeric(dat_test$label)-1
#Rebalancing training data-Bootstrap Random Over-Sampling Examples Technique (ROSE) source
if(run.balanced.data){
dat_train_balanced_rose<-ROSE(label~., dat_train,seed=2020)$data
save(dat_train_balanced_rose, file="../output/balanced_data.RData")
}else{
  load(file="../output/balanced_data.RData")
}
table(dat_train_balanced_rose$label)
```

### Step 4: Train a classification model with training features and responses
Call the train model and test model from library. 

`train.R` and `test.R` should be wrappers for all your model training steps and your classification/prediction steps. 

+ `train.R`
  + Input: a data frame containing features and labels and a parameter list.
  + Output:a trained model
+ `test.R`
  + Input: the fitted classification model using training data and processed features from testing images 
  + Input: an R object that contains a trained classifier.
  + Output: training model specification

+ In this Starter Code, we use logistic regression with LASSO penalty to do classification. 

```{r loadlib}
source("../lib/train.R") 
source("../lib/test.R")
```

#### Model selection with cross-validation
* Do model selection by choosing among different values of training model parameters.

```{r runcv}
source("../lib/cross_validation.R")
feature_train = as.matrix(dat_train[, 1:ncol(dat_train)-1])
label_train = as.integer(dat_train$label)
if(run.cv){
  res_cv <- matrix(0, nrow = length(lmbd), ncol = 4)
  for(i in 1:length(lmbd)){
    cat("lambda = ", lmbd[i], "\n")
    res_cv[i,] <- cv.function(features = feature_train, labels = label_train, K, 
                              l = lmbd[i], reweight = sample.reweight)
  save(res_cv, file="../output/res_cv.RData")
  }
}else{
  load("../output/res_cv.RData")
}
```


Visualize cross-validation results.
```{r cv_vis}
# res_cv_rf  <- as.data.frame(res_cv_rf )
# colnames(res_cv_rf ) <- c("mean_error", "sd_error", "mean_AUC", "sd_AUC")
# res_cv_rf$k = as.factor(lmbd)
# 
# if(run.cv){
#   p1 <- res_cv_rf  %>%
#     ggplot(aes(x = as.factor(lmbd), y = mean_error,
#                ymin = mean_error - sd_error, ymax = mean_error + sd_error)) +
#     geom_crossbar() +
#     theme(axis.text.x = element_text(angle = 90, hjust = 1))
# 
#   p2 <- res_cv_rf  %>%
#     ggplot(aes(x = as.factor(lmbd), y = mean_AUC,
#                ymin = mean_AUC - sd_AUC, ymax = mean_AUC + sd_AUC)) +
#     geom_crossbar() +
#     theme(axis.text.x = element_text(angle = 90, hjust = 1))
# 
#   print(p1)
#   print(p2)
# }
# lambda=0.01 is the best
```


* Choose the "best" parameter value
```{r best_model}
#par_best <- lmbd[which.min(res_cv_rf$mean_error)] # lmbd[which.max(res_cv$mean_AUC)]
```

# Advanced Models:
Create weight test
```{r}
label_test <- as.integer(dat_test$label)
weight_test <- rep(NA, length(label_test))
for (i in unique(label_test)){
  weight_test[label_test == i] = 0.5 * length(label_test) / length(label_test[label_test == i])
}
# training weights
weight_train <- rep(NA, length(label_train))
for (v in unique(label_train)){
  weight_train[label_train == v] = 0.5 * length(label_train) / length(label_train[label_train == v])
}

if (run.gbm){
  if (sample.reweight){
    tm_train <- system.time(fit_train <- train_gbm(dat_train, s=0.1, K=K, n=gbm.numtrees,w = weight_train))
  } else {
    tm_train <- system.time(fit_train <- train_gbm(dat_train, s=0.1, K=K, n=gbm.numtrees,w = NULL))
  }
  
  # plot the performance
  best.iter.oob <- gbm.perf(fit_train,method="OOB")  # returns out-of-bag estimated best number of trees
  print(best.iter.oob)
  best.iter.cv <- gbm.perf(fit_train,method="cv")   # returns K-fold cv estimate of best number of trees
  print(best.iter.cv)

} else {
  if (sample.reweight){
    tm_train <- system.time(fit_train <- train(feature_train, label_train, w = weight_train, par_best))
  } else {
    tm_train <- system.time(fit_train <- train(feature_train, label_train, w = NULL, par_best))
  }
}
save(fit_train, file="../output/fit_train.RData")
```



### Step 5: Run test on test images
```{r test}
tm_test = NA
feature_test <- as.matrix(dat_test[, 1:ncol(dat_test)-1])
if(run.test){
  load(file="../output/fit_train.RData")
  if (run.gbm){
    tm_test <- system.time(prob_pred<-test_gbm(fit_train,as.data.frame(feature_test),n=best.iter.cv,pred.type = 'response'))
    
    label_pred <- colnames(prob_pred)[apply(prob_pred, 1, which.max)]
    
  } else {
    tm_test <- system.time({label_pred <- as.integer(test(fit_train, feature_test, pred.type = 'class')); 
                            prob_pred <- test(fit_train, feature_test, pred.type = 'response')})  
  }
  
}
```

Random Forest:

## Tune RF
```{r}
source("../lib/random_forest.R")
if(tune.random.forest){
time.rf.tune <- system.time(rf.tune <- random_forest_tune(dat_train_balanced_rose))
save(rf.tune, file="../output/rf_tune.RData")
}else(
  load("../output/rf_tune.RData")
)
rf.tune
```
mtry = 154 is the best.

## Find the best ntrees
```{r}
source("../lib/random_forest.R")

#Train 500
if(tune.random.forest){
time.rf.train <- system.time(random_forest_fit_500 <- random_forest_train_500(dat_train_balanced_rose,mtry = 154))
save(random_forest_fit_500, file = "../output/rf_train_500_trees.RData")
}
#Test 500
random_forest_test_prep=NA
if(tune.random.forest){
 load(file="../output/rf_train_500_trees.RData")
 time.rf.test <- system.time(
   random_forest_test_prep <- random_forest_test(
     model = random_forest_fit_500,testset = dat_test)
               )

random_forest_test_prep <- as.numeric(as.character(random_forest_test_prep))
accu_rf_test <- mean(random_forest_test_prep == dat_test$label)
random_forest_label<-round(random_forest_test_prep)
accu_rf <- sum(weight_test * (random_forest_label == label_test)) / sum(weight_test)
#prob_pred <- lable_pred
tpr.fpr <- WeightedROC(random_forest_test_prep, label_test, weight_test)
auc_rf <- WeightedAUC(tpr.fpr)
cat("The AUC of model after reweighting: RF", "is", auc_rf, ".\n")
cat("The accuracy of model: Random Forest on imbalanced testing data", "is", accu_rf_test*100, "%.\n")
cat("The accuracy of model: Random Forest on balanced testing data", "is", accu_rf*100, "%.\n")
cat("Time for training model Random Forest = ", time.rf.train[1], "s \n")
cat("Time for testing model Random Forest = ",time.rf.test[1], "s \n")
}
# The AUC of model after reweighting: RF is 0.5031999 .
# The accuracy of model: Random Forest on imbalanced testing data is 80.33333 %.
# The accuracy of model: Random Forest on balanced testing data is 50.31999 %.
# Time for training model Random Forest =  20.95 s 
# Time for testing model Random Forest =  0.09 s 

#Train 1000
if(tune.random.forest){
time.rf.train <- system.time(random_forest_fit_1000 <- random_forest_train_1000(dat_train_balanced_rose,mtry = 154))
save(random_forest_fit_1000, file = "../output/rf_train_1000_trees.RData")
}
#Test 1000
random_forest_test_prep=NA
if(tune.random.forest){
 load(file="../output/rf_train_1000_trees.RData")
 time.rf.test <- system.time(
   random_forest_test_prep <- random_forest_test(
     model = random_forest_fit_1000,testset = dat_test)
               )

random_forest_test_prep <- as.numeric(as.character(random_forest_test_prep))
accu_rf_test <- mean(random_forest_test_prep == dat_test$label)
random_forest_label<-round(random_forest_test_prep)
accu_rf <- sum(weight_test * (random_forest_label == label_test)) / sum(weight_test)
#prob_pred <- lable_pred
tpr.fpr <- WeightedROC(random_forest_test_prep, label_test, weight_test)
auc_rf <- WeightedAUC(tpr.fpr)
cat("The AUC of model after reweighting: RF", "is", auc_rf, ".\n")
cat("The accuracy of model: Random Forest on imbalanced testing data", "is", accu_rf_test*100, "%.\n")
cat("The accuracy of model: Random Forest on balanced testing data", "is", accu_rf*100, "%.\n")
cat("Time for training model Random Forest = ", time.rf.train[1], "s \n")
cat("Time for testing model Random Forest = ",time.rf.test[1], "s \n")
}

#Train 1500
if(tune.random.forest){
time.rf.train <- system.time(random_forest_fit_1500 <- random_forest_train_1500(dat_train_balanced_rose,mtry = 154))
save(random_forest_fit_1500, file = "../output/rf_train_1500_trees.RData")
}
#Test 1500
random_forest_test_prep=NA
if(tune.random.forest){
 load(file="../output/rf_train_1500_trees.RData")
 time.rf.test <- system.time(
   random_forest_test_prep <- random_forest_test(
     model = random_forest_fit_1500,testset = dat_test)
               )

random_forest_test_prep <- as.numeric(as.character(random_forest_test_prep))
accu_rf_test <- mean(random_forest_test_prep == dat_test$label)
random_forest_label<-round(random_forest_test_prep)
accu_rf <- sum(weight_test * (random_forest_label == label_test)) / sum(weight_test)
#prob_pred <- lable_pred
tpr.fpr <- WeightedROC(random_forest_test_prep, label_test, weight_test)
auc_rf <- WeightedAUC(tpr.fpr)
cat("The AUC of model after reweighting: RF", "is", auc_rf, ".\n")
cat("The accuracy of model: Random Forest on imbalanced testing data", "is", accu_rf_test*100, "%.\n")
cat("The accuracy of model: Random Forest on balanced testing data", "is", accu_rf*100, "%.\n")
cat("Time for training model Random Forest = ", time.rf.train[1], "s \n")
cat("Time for testing model Random Forest = ",time.rf.test[1], "s \n")
}

#Train 2000
if(tune.random.forest){
time.rf.train <- system.time(random_forest_fit_2000 <- random_forest_train_2000(dat_train_balanced_rose,mtry = 154))
save(random_forest_fit_2000, file = "../output/rf_train_2000_trees.RData")
}
#Test 2000
random_forest_test_prep=NA
if(tune.random.forest){
 load(file="../output/rf_train_2000_trees.RData")
 time.rf.test <- system.time(
   random_forest_test_prep <- random_forest_test(
     model = random_forest_fit_2000,testset = dat_test)
               )

random_forest_test_prep <- as.numeric(as.character(random_forest_test_prep))
accu_rf_test <- mean(random_forest_test_prep == dat_test$label)
random_forest_label<-round(random_forest_test_prep)
accu_rf <- sum(weight_test * (random_forest_label == label_test)) / sum(weight_test)
#prob_pred <- lable_pred
tpr.fpr <- WeightedROC(random_forest_test_prep, label_test, weight_test)
auc_rf <- WeightedAUC(tpr.fpr)
cat("The AUC of model after reweighting: RF", "is", auc_rf, ".\n")
cat("The accuracy of model: Random Forest on imbalanced testing data", "is", accu_rf_test*100, "%.\n")
cat("The accuracy of model: Random Forest on balanced testing data", "is", accu_rf*100, "%.\n")
cat("Time for training model Random Forest = ", time.rf.train[1], "s \n")
cat("Time for testing model Random Forest = ",time.rf.test[1], "s \n")
}
#Train 2500
if(tune.random.forest){
time.rf.train <- system.time(random_forest_fit_2500 <- random_forest_train_2500(dat_train_balanced_rose,mtry = 154))
save(random_forest_fit_2500, file = "../output/rf_train_2500_trees.RData")
}
#Test 2500
random_forest_test_prep=NA
if(tune.random.forest){
 load(file="../output/rf_train_2500_trees.RData")
 time.rf.test <- system.time(
   random_forest_test_prep <- random_forest_test(
     model = random_forest_fit_2500,testset = dat_test)
               )

random_forest_test_prep <- as.numeric(as.character(random_forest_test_prep))
accu_rf_test <- mean(random_forest_test_prep == dat_test$label)
random_forest_label<-round(random_forest_test_prep)
accu_rf <- sum(weight_test * (random_forest_label == label_test)) / sum(weight_test)
#prob_pred <- lable_pred
tpr.fpr <- WeightedROC(random_forest_test_prep, label_test, weight_test)
auc_rf <- WeightedAUC(tpr.fpr)
cat("The AUC of model after reweighting: RF", "is", auc_rf, ".\n")
cat("The accuracy of model: Random Forest on imbalanced testing data", "is", accu_rf_test*100, "%.\n")
cat("The accuracy of model: Random Forest on balanced testing data", "is", accu_rf*100, "%.\n")
cat("Time for training model Random Forest = ", time.rf.train[1], "s \n")
cat("Time for testing model Random Forest = ",time.rf.test[1], "s \n")
}

```
Testing Result:
When trees = 500:
The AUC of model after reweighting: RF is 0.5116745 .
The accuracy of model: Random Forest on imbalanced testing data is 80.66667 %.
The accuracy of model: Random Forest on balanced testing data is 51.16745 %.
Time for training model Random Forest =  713.63 s 
Time for testing model Random Forest =  0.19 s 

When trees = 1000
The AUC of model after reweighting: RF is 0.5201491 .
The accuracy of model: Random Forest on imbalanced testing data is 81 %.
The accuracy of model: Random Forest on balanced testing data is 52.01491 %.
Time for training model Random Forest =  1367.94 s 
Time for testing model Random Forest =  0.28 s 

When trees = 1500
The AUC of model after reweighting: RF is 0.5201491 .
The accuracy of model: Random Forest on imbalanced testing data is 81 %.
The accuracy of model: Random Forest on balanced testing data is 52.01491 %.
Time for training model Random Forest =  2077.56 s 
Time for testing model Random Forest =  0.36 s 

When trees = 2000
The AUC of model after reweighting: RF is 0.5201491 .
The accuracy of model: Random Forest on imbalanced testing data is 81 %.
The accuracy of model: Random Forest on balanced testing data is 52.01491 %.
Time for training model Random Forest =  3142.77 s 
Time for testing model Random Forest =  0.56 s 

When trees = 2500
The AUC of model after reweighting: RF is 0.5159118 .
The accuracy of model: Random Forest on imbalanced testing data is 80.83333 %.
The accuracy of model: Random Forest on balanced testing data is 51.59118 %.
Time for training model Random Forest =  3963.67 s 
Time for testing model Random Forest =  0.62 s 

Therefore, we should use trees = 1000.

## Train RF with tuning parameters:
```{r}
source("../lib/random_forest.R")
if(train.random.forest){
  time.rf.train <- system.time(random_forest_fit <- random_forest_train(dat_train_balanced_rose,mtry = 154))
  save(random_forest_fit, file = "../output/random_forest_train.RData")
  save(time.rf.train,file = "../output/random_forest_train_time.RData")
}else{
  load(file = "../output/random_forest_train_time.RData")
  load(file = "../output/random_forest_train.RData")
}
```
## Test RF with tuning parameters
```{r}
random_forest_test_prep=NA
if(run.test){
 load(file="../output/random_forest_train.RData")
 time.rf.test <- system.time(
   random_forest_test_prep <- random_forest_test(
     model = random_forest_fit,testset = dat_test)
               )
}
## reweight the test data to represent a balanced label distribution
if (run.gbm){
  accu <- mean(dat_test$label == label_pred)
  cat("The accuracy of GBM baseline model is", mean(dat_test$label == label_pred)*100, "%.\n")
  
} else {
  label_test <- as.integer(dat_test$label)
  weight_test <- rep(NA, length(label_test))
  for (v in unique(label_test)){
    weight_test[label_test == v] = 0.5 * length(label_test) / length(label_test[label_test == v])
  }
  
  accu <- sum(weight_test * (label_pred == label_test)) / sum(weight_test)
  tpr.fpr <- WeightedROC(prob_pred, label_test, weight_test)
  auc <- WeightedAUC(tpr.fpr)
  
  
  cat("The accuracy of model:", model_labels[which.min(res_cv$mean_error)], "is", accu*100, "%.\n")
  cat("The AUC of model:", model_labels[which.min(res_cv$mean_error)], "is", auc, ".\n")
}
random_forest_test_prep <- as.numeric(as.character(random_forest_test_prep))
accu_rf_test <- mean(random_forest_test_prep == dat_test$label)
```
## Calculate weightedAUC on testing split
```{r}
random_forest_label<-round(random_forest_test_prep)
#prob_pred <- lable_pred
tpr.fpr <- WeightedROC(random_forest_test_prep, label_test, weight_test)
auc_rf <- WeightedAUC(tpr.fpr)
```
## Summary of RF
```{r}
cat("The AUC of model after reweighting: RF", "is", auc_rf, ".\n")
cat("The accuracy of model: Random Forest on testing data", "is", accu_rf_test*100, "%.\n")
cat("Time for training model Random Forest = ", time.rf.train[1], "s \n")
cat("Time for testing model Random Forest = ",time.rf.test[1], "s \n")
#label_test


cat("The accuracy of model:", model_labels[which.min(res_cv$mean_error)], "is", accu*100, "%.\n")
cat("The AUC of model:", model_labels[which.min(res_cv$mean_error)], "is", auc, ".\n")
```

### Summarize Running Time
Prediction performance matters, so does the running times for constructing features and for training the model, especially when the computation resource is limited. 
```{r running_time}
# cat("Time for constructing training features=", tm_feature_train[1], "s \n")
# cat("Time for constructing testing features=", tm_feature_test[1], "s \n")
# cat("Time for training model=", tm_train[1], "s \n") 
# cat("Time for testing model=", tm_test[1], "s \n")
```


### SVM Model

* Balance the Training Data

```{r}
library(ROSE)
tm_svm_rebalanced_train <- NA
if(needs.balanced){
  tm_svm_rebalanced_train <- system.time(svm_training_data <- ROSE(label ~ ., data = dat_train)$data)
  save(svm_training_data, file="../output/svm_training_data.RData")
} else {
  load(file="../output/svm_training_data.RData")
}
```


* Model Selection

```{r}
library(e1071)
tm_svm_default_mod <- NA
tm_svm_linear_cost <-NA
tm_svm_linear_mod <- NA
if(model.selection){
  svm_model_auc <- rep(NA, 2)
  # default model
  if(run.cv){
    tm_svm_default_mod < system.time(svm_default_mod <- svm_default_train(svm_training_data, K))
    save(svm_default_mod, file="../output/svm_default_mod.RData")
  } else {
    load(file="../output/svm_default_mod.RData")
  }
  svm_default_pred <- svm_test(svm_default_mod, svm_training_data)
  #mean(round(svm_default_pred == svm_training_data$label))
  tpr.fpr_default <- WeightedROC(as.numeric(svm_default_pred), svm_training_data$label)
  svm_model_auc[1] <- WeightedAUC(tpr.fpr_default)
  
  
  # linear kernel
  if(run.cv){
    tm_svm_linear_cost < system.time(best.cost <- svm.cv.linear(svm_training_data, K))
    tm_svm_linear_mod <- system.time(svm_linear_mod <- svm_linear_train(svm_training_data, best.cost, K))
    save(svm_linear_mod, file="../output/svm_linear_mod.RData")
  } else {
    load(file="../output/svm_linear_mod.RData")
  }
  svm_linear_pred <- svm_test(svm_linear_mod, svm_training_data)
  #mean(round(svm_linear_pred == svm_training_data$label))
  tpr.fpr_linear <- WeightedROC(as.numeric(svm_linear_pred), svm_training_data$label)
  svm_model_auc[2] <- WeightedAUC(tpr.fpr_linear)
  
  
  # select model with the highest auc
  curr_best_auc <- which.max(svm_model_auc)
  if(curr_best_auc == 1){
    svm_best_mod <- svm_default_mod
    save(svm_best_mod, file="../output/svm_best_mod.RData")
  } else{
    svm_best_mod <- svm_linear_mod
    save(svm_best_mod, file="../output/svm_best_mod.RData")
  }
} else{
  load(file="../output/svm_best_mod.RData")
}
```

* Evaluation on Testing Data

```{r}
tm_svm_rebalanced_test <- NA
if(needs.balanced){
  tm_svm_rebalanced_test <- system.time(svm_testing_data <- ROSE(label ~ ., data = dat_test)$data)
  save(svm_testing_data, file="../output/svm_testing_data.RData")
} else {
  load(file="../output/svm_testing_data.RData")
}
tm_svm_test <- system.time(svm_pred <- svm_test(svm_linear_mod, svm_testing_data))

svm_accu = mean(round(svm_pred == svm_testing_data$label))
tpr.fpr <- WeightedROC(as.numeric(svm_pred), svm_testing_data$label)
svm_auc = WeightedAUC(tpr.fpr)

cat("The accuracy of svm model is", svm_accu*100, "%.\n")
cat("The AUC of svm model is", svm_auc, ".\n")
```

* Summarize Running Time

```{r}
cat("Time for rebalancing training data =", tm_svm_rebalanced_train[1], "s \n")
cat("Time for rebalancing testing data =", tm_svm_rebalanced_test[1], "s \n")
cat("Time for training model =", tm_svm_linear_mod[1], "s \n")
cat("Time for testing model=", tm_svm_test[1], "s \n")
```


## ridge model

### apply constructed ridge model to the training data
```{r}
tm_ridge_train <- NA
if (train.ridge){
  dat_train_rebalanced <- ROSE(label ~ ., data = dat_train, seed=2021)$data
  tm_ridge_train <- system.time(ridge_cv_model<-ridge_train(train_data=dat_train_rebalanced, alpha=alpha, K=K, lambda=lambda))
  save(ridge_cv_model, file="../output/ridge_cv_model.RData")
  save(tm_ridge_train, file="../output/ridge_train_time.RData")
}else{
  load(file="../output/ridge_cv_model.RData")
  load(file="../output/ridge_train_time.RData")
}
```

### use cross-validation to choose the optimal lambda with smallest MSE
```{r}
if (run.cv){
  set.seed(2020)
  feature_train = as.matrix(dat_train[, -6007])
  label_train = as.integer(dat_train$label)
  ridge_model = cv.glmnet(x=feature_train, y=label_train, alpha=alpha, nfolds=K, lambda=lambda)
  opt_lambda = ridge_model$lambda.min
  save(opt_lambda, file="../output/ridge_optimal_lambda.RData")
}else{
  load(file="../output/ridge_optimal_lambda.RData")
}
```

### predict testing data with the optimal lambda
```{r}
tm_ridge_test = NA
if(run.test){
  load("../output/ridge_cv_model.RData")
  feature_test <- as.matrix(dat_test[, -6007])
  tm_ridge_test <- system.time(label_pred<-as.integer(ridge_test(model=ridge_cv_model, features=feature_test, pred.type = 'class')))
  save(tm_ridge_test, file="../output/ridge_test_time.RData")
} else{
  load(file="../output/ridge_test_time.RData")
}
```

### summarize running time
```{r}
cat("Time for constructing training features=", tm_feature_train[1], "s \n")
cat("Time for constructing testing features=", tm_feature_test[1], "s \n")
cat("Time for training ridge model=", tm_ridge_train[1], "s \n") 
cat("Time for testing ridge model=", tm_ridge_test[1], "s \n")
```

### run evaluation on independent testing data 
```{r}
load("../output/ridge_cv_model.RData")
feature_test <- as.matrix(dat_test[, -6007])
label_pred = as.integer(predict(ridge_cv_model, s=opt_lambda, newx=feature_test, type='class'))
label_test = as.integer(dat_test$label)
compare <- cbind (label_test, label_pred)
ridge_accuracy = mean(apply(compare, 1, min)/apply(compare, 1, max)) 
cat("The accuracy of the ridge model is", ridge_accuracy*100, "%.\n")
ridge_AUC = auc(roc(label_pred,label_test))
cat("The AUC of the ridge model is", ridge_AUC, ".\n")
```


###Reference
- Du, S., Tao, Y., & Martinez, A. M. (2014). Compound facial expressions of emotion. Proceedings of the National Academy of Sciences, 111(15), E1454-E1462.
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